黑胶复兴不是新闻,但只适合在家享受,遛狗和通勤时,你该如何与Spotify说“再见”?你能想象吗?诞生于1960年代的卡式录音带正迎来其“复兴”时刻——流行乐歌手推出卡带版新作;受索尼Walkman启发(也许很多读者是第一次听到这个专有名词),...黑胶复兴不是新闻,但只适合在家享受,遛狗和通勤时,你该如何与Spotify说“再见”?你能想象吗?诞生于1960年代的卡式录音带正迎来其“复兴”时刻——流行乐歌手推出卡带版新作;受索尼Walkman启发(也许很多读者是第一次听到这个专有名词),We Are Rewind(15999美刀)、FiO(124.99美刀)和亚马逊上众多杂牌,纷纷推出了卡带随身听。展开更多
Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance ...Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance for prediction.The publicly accessible Kaggle data consists of 92,755 rows and 20 columns.Using multiple model comparisons for regression and classification,this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy.This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song’s popularity using both classification and regression machine learning methods,with an emphasis on Indonesia based on consumer culture theory.展开更多
文摘黑胶复兴不是新闻,但只适合在家享受,遛狗和通勤时,你该如何与Spotify说“再见”?你能想象吗?诞生于1960年代的卡式录音带正迎来其“复兴”时刻——流行乐歌手推出卡带版新作;受索尼Walkman启发(也许很多读者是第一次听到这个专有名词),We Are Rewind(15999美刀)、FiO(124.99美刀)和亚马逊上众多杂牌,纷纷推出了卡带随身听。
文摘Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance for prediction.The publicly accessible Kaggle data consists of 92,755 rows and 20 columns.Using multiple model comparisons for regression and classification,this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy.This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song’s popularity using both classification and regression machine learning methods,with an emphasis on Indonesia based on consumer culture theory.
