Treffer: Enhancing spotify most streamed song prediction through Resnet-50 over Bayesian Regression.

Improving the performance of Spotify most streamed song prediction is the major goal of this study. The study made use of the kaggle dataset as the primary source of data. Two distinct groups, Group I and Group 2, each comprising 20 samples, were utilised in this study. Group I employed the Resnet-50, while Group 2 utilised the Bayesian Regression. The total sample size for the study was 40. Sample size calculations for statistical analysis, as well as the subsequent performance comparison were conducted and implementation was done using Python. The statistical analysis was carried out using clincalc.com with a statistical power (G-power) set at 85%, alpha (a) at 0.05, beta (13) at 0.2. The analysis primarily focused on comparing the performance of the Resnet-50 and Algorithm using accuracy value as the key evaluation metric. In terms of accuracy, Resnet-50 (94.843%) outperforms Bayesian Regression (80.503%), with a two-tailed, p>0.05 significance value of <.001. In summary, the accuracy of Resnet-50 outperforms Bayesian Regression accuracy. [ABSTRACT FROM AUTHOR]

Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)