SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify
I Dimolitsas, S Kantarelis, A Fouka - arXiv preprint arXiv:2301.07978, 2023 - arxiv.org
I Dimolitsas, S Kantarelis, A Fouka
arXiv preprint arXiv:2301.07978, 2023•arxiv.orgIn 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.
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.
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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