Abstract
The aim of this research is to analyze the dataset of 42305 songs and 15 different genres on the Spotify music platform and examine the relationship of the song with the genres. Relationships with these species were analyzed from the dataset as a preliminary assessment for the species prediction study. The features of the species in the data set are evaluated and categorically according to their features, from data mining classification algorithms; Nearest K-Neighbor, random forest, bagging and logistic regression were used. The study was carried out to predict the types of songs according to the characteristics of the song. Accuracy values between 55% and 77% were obtained. A model with the best performance measurement value of the classification algorithms was considered and the results were evaluated.
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