# Song Hit Prediction: Predicting Billboard Hits Using Spotify Data

arXiv: https://arxiv.org/abs/1908.08609 · HTML: https://mlsanity.com/paper/1908.08609/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify](https://mlsanity.com/paper/2301.07978/) (2023)
- [An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features](https://mlsanity.com/paper/2301.13507/) (2023)
- [Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features](https://mlsanity.com/paper/2509.24856/) (2025)
- [APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music](https://mlsanity.com/paper/2605.03395/) (2026)

## 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.