Song Hit Prediction: Predicting Billboard Hits Using Spotify Data
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Abstract (arXiv)
In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model was random forest, which was able to predict Billboard song success with 88% accuracy.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2019.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotifycited by 2
- 2023An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Featurescited by 2
- 2025Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Featurescited by 0
- 2026APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Musiccited by 0
- 2026APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music5 May 2026
- 2025Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features29 Sep 2025
- 2023An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features31 Jan 2023
- 2023SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify19 Jan 2023