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Song Hit Prediction: Predicting Billboard Hits Using Spotify Data
In this work, we attempt to solve the Hit Song Science problem, which ai...
08/22/2019 ∙ by Kai Middlebrook, et al. ∙
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Predicting Eating Events in Free Living Individuals -- A Technical Report
This technical report records the experiments of applying multiple machi...
08/14/2019 ∙ by Jiayi Wang, et al. ∙
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A random forest based approach for predicting spreads in the primary catastrophe bond market
We introduce a random forest approach to enable spreads' prediction in t...
01/28/2020 ∙ by Despoina Makariou, et al. ∙
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Random boosting and random^2 forests – A random tree depth injection approach
The induction of additional randomness in parallel and sequential ensemb...
09/13/2020 ∙ by Tobias Markus Krabel, et al. ∙
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An Evaluation of Classification and Outlier Detection Algorithms
This paper evaluates algorithms for classification and outlier detection...
05/02/2018 ∙ by Victoria J. Hodge, et al. ∙
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Which Pull Requests Get Accepted and Why? A study of popular NPM Packages
Background: Pull Request (PR) Integrators often face challenges in terms...
03/02/2020 ∙ by Tapajit Dey, et al. ∙
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A Novel Approach to Radiometric Identification
This paper demonstrates that highly accurate radiometric identification ...
12/02/2020 ∙ by Raoul Nigmatullin, et al. ∙
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Predicting Afrobeats Hit Songs Using Spotify Data
07/07/2020 ∙ by Adewale Adeagbo, et al. ∙
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This study approached the Hit Song Science problem with the aim of predicting which songs in the Afrobeats genre will become popular among Spotify listeners. A dataset of 2063 songs was generated through the Spotify Web API, with the provided audio features. Random Forest and Gradient Boosting algorithms proved to be successful with approximately F1 scores of 86
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