Prediction of Spotify Chart Success Using Audio and Streaming Features

TL;DR

This study develops a machine learning pipeline to predict Spotify chart success using audio features and early engagement data, demonstrating high accuracy and robustness across models, with implications for music marketing and A&R strategies.

Contribution

The paper introduces a comprehensive classification approach that combines audio and streaming data to accurately forecast a song's chart success, highlighting the predictive power of audio features alone.

Findings

Tree-based models achieved near 0.95 macro F1-score and 97% accuracy.

Audio features alone can predict chart success without streaming data.

Models remain effective even without stream count and rank history.

Abstract

Spotify's streaming charts offer a real-time lens into music popularity, driving discovery, playlists, and even revenue potential. Understanding what influences a song's rise in ranks on these charts-especially early on-can guide marketing efforts, investment decisions, and even artistic direction. In this project, we developed a classification pipeline to predict a song's chart success based on its musical characteristics and early engagement data. Using all 2024 U.S. Top 200 Spotify Daily Charts and the Spotify Web API, we built a dataset containing both metadata and audio features for 14,639 unique songs. The project was structured in two phases. First, we benchmarked four models: Logistic Regression, K Nearest Neighbors, Random Forest, and XGBoost-using a standard train-test split. In the second phase, we incorporated cross-validation, hyperparameter tuning, and detailed class-level evaluation to ensure robustness. Tree-based models consistently outperformed the rest, with Random Forest and XGBoost achieving macro F1-scores near 0.95 and accuracy around 97%. Even when stream count and rank history were excluded, models trained solely on audio attributes retained predictive power. These findings validate the potential of audio-based modeling in A&R scouting, playlist optimization, and hit forecasting-long before a track reaches critical mass.