Müzik Veri Setinin Analizi ve Sınıflandırma Algoritmaları Kullanılarak Şarkı Türü Tahminleme Çalışması

Year 2022, Issue: 40, 143 - 150, 30.09.2022

Berke Bartuğ Sevindik Vahide Bulut

https://doi.org/10.31590/ejosat.1174115

Abstract

Bu araştırmanın amacı, Spotify müzik platformunda yer alan 42305 şarkı ve 15 farklı türe sahip veri setini analiz edip şarkının
türlerle olan ilişkisini incelemektir. Bu türlerle olan ilişkiler veri setinden tür tahminleme çalışması için ön değerlendirme olarak analiz
edilmiştir. Veri setindeki türlere ait özellikler değerlendirilip, kategorik olarak özelliklerine göre veri madenciliği sınıflandırma
algoritmalarından; En yakın K-Komşu, rastgele orman, torbalama ve lojistik regresyon kullanılmıştır. Şarkının özelliklerine göre
şarkıların türlerini tahmin etme çalışması gerçekleştirilmiştir. %55 ve %77 arasında doğruluk değerleri elde edilmiştir. Sınıflandırma
algoritmalarının en iyi performans ölçüm değerine sahip bir model ele alınarak sonuçları değerlendirilmiştir.

Keywords

Tahminleme, Analiz, En yakın k-komşu, Rasgele orman, torbalama, Lojistik regresyon, Sınıflandırma algoritmaları

Song Genre Estimation Study Using Music Data Set Analysis and Classification Algorithms

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.

Keywords

Prediction, Analysis, K-nearest k-neighbors, Random forest, Logistic regression, bagging, Classification algorithms

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