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

Ioannis Dimolitsas, Spyridon Kantarelis, Afroditi Fouka
1/19/2023
cs.SDcs.LGeess.AS

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Abstract

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.

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Cite this paper

@article{dimolitsas2023spothitpy,
  title  = {SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify},
  author = {Ioannis Dimolitsas and Spyridon Kantarelis and Afroditi Fouka},
  year   = {2023},
  eprint = {2301.07978},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.07978v1}
}

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