Treffer: Analysing the performance of spotify most streamed song prediction using Resnet-50 in Comparison with KNN.

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 K Nearest Neighbour. 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 K Nearest Neighbour (79.705%), with a two-tailed, p>0.05 significance value of <.001. In summary, the accuracy of Resnet-50 outperforms K Nearest Neighbour accuracy. [ABSTRACT FROM AUTHOR]