![Song Hit Prediction](https://writelatex.s3.amazonaws.com/published_ver/11076.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240831T230709Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240831/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=a53927241f7a78c6e4edf93b875741b06db64acfdcaedeb637ab8584129e02cf)
Song Hit Prediction
Auteur
Kai Middlebrook, Kian Sheik
Last Updated
il y a 5 ans
License
Creative Commons CC BY 4.0
Résumé
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.
![Song Hit Prediction](https://writelatex.s3.amazonaws.com/published_ver/11076.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240831T230709Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240831/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=a53927241f7a78c6e4edf93b875741b06db64acfdcaedeb637ab8584129e02cf)