# SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify

arXiv: https://arxiv.org/abs/2301.07978 · HTML: https://mlsanity.com/paper/2301.07978/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Revisiting the problem of audio-based hit song prediction using convolutional neural networks](https://mlsanity.com/paper/1704.01280/) (2017)
- [Song Hit Prediction: Predicting Billboard Hits Using Spotify Data](https://mlsanity.com/paper/1908.08609/) (2019)

## Built on by (most cited first, 2 of 2)

- [Beyond Beats: A Recipe to Song Popularity? A machine learning approach](https://mlsanity.com/paper/2403.12079/) (2024)
- [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)

## Abstract (arXiv)

In this study, we approached the Hit Song Prediction problem, which aims to predict which songs will become Billboard hits. We gathered a dataset of nearly 18500 hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four machine-learning models on our dataset. We were able to predict the Billboard success of a song with approximately 86% accuracy. The most succesful algorithms were Random Forest and Support Vector Machine.