An in-depth exploration of Spotify's most streamed songs for the year 2023. This analysis aims to determine key features contributing to a song's success using a multifaceted dataset, with data points ranging from song characteristics to performance across different streaming platforms.
Sourced from Kaggle, this dataset provides insights not only about Spotify but also extends to platforms like Apple Music, Deezer, and Shazam.
- Track & Artist Information: Covers track name, artist name, artist count, track type, and release date.
- Platform Performance: Metrics for Spotify, Apple Music, Deezer, and Shazam.
- Song Attributes: Beats per minute, musical key, and more.
- Custom Ranks: Based on varying criteria.
- Logistic Regression: Determined the significance of various song attributes on their success.
- Logistic Regression with Grid Search: Optimized hyperparameters for best performance.
- K-Fold Validation: Ensured robust model validation by partitioning the dataset multiple times.
- KNN with Grid Search: K-Nearest Neighbors algorithm further optimized with grid search.
- Models stacked:
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVC)
- Establish if a song's modality (Major or Minor) predicts its success.
- Understand the representation of Major and Minor tracks across different playlist types.
- Dive deep into the predictors of a song's success across various streaming platforms.
Dive into the data insights and graphical representations to understand the trends, artist performances, and song attributes that resonate most with the audience.
Analyst: Ola76
For collaborations, feedback, or questions, please reach out.