Research Article
Harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry
@INPROCEEDINGS{10.1007/978-3-031-48888-7_3, author={Deepesh Trivedi and Manas Saxena and S. S. P. M. Sharma B and Indrajeet Kumar}, title={Harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Analysis exploratory data analysis machine-learning techniques spotify}, doi={10.1007/978-3-031-48888-7_3} }- Deepesh Trivedi
Manas Saxena
S. S. P. M. Sharma B
Indrajeet Kumar
Year: 2024
Harmonizing Insights: Python-Based Data Analysis of Spotify's Musical Tapestry
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_3
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.
