Harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry Copy

@InProceedings{10.1007/978-3-031-48888-7_3,

author="Trivedi, Deepesh

and Saxena, Manas

and Sharma B, S. S. P. M.

and Kumar, Indrajeet",

editor="Pareek, Prakash

and Gupta, Nishu

and Reis, M. J. C. S.",

title="Harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry",

booktitle="Cognitive Computing and Cyber Physical Systems",

year="2024",

publisher="Springer Nature Switzerland",

address="Cham",

pages="28--44",

abstract="This research paper analysis Spotify data using Python to investigate the characteristics contributing to song popularity. The objectives are to assess the popularity index, identify key attributes of popular songs, and develop a model for predicting song popularity based on current characteristics. The analysis involves data cleaning, exploratory data analysis, and visualization using Python libraries. With over 381 million monthly active users, Spotify provides a rich dataset for understanding music listening habits. Previous studies have explored Spotify's technologies and popularity, enhancing understanding of its protocols and user behavior. This research paper aims to uncover patterns and relationships within the data by applying statistical and machine-learning techniques. The findings will inform actionable recommendations and contribute to a better understanding of music consumption patterns and preferences.",

isbn="978-3-031-48888-7"

}