This project utilizes data analysis techniques to extract meaningful insights from a user's Spotify listening history, in order to provide personalized recommendations for music artists, genres, and songs. By analyzing the user's music preferences, the project identifies their top artists and genres, as well as their most frequently played songs.
One of the key advantages of this project is that it offers an alternative to Spotify's "Wrapped" feature, which is a year-end summary of a user's listening habits, but is limited to premium account holders. In contrast, this project is accessible to all Spotify users, regardless of their subscription status.
To get an idea of how the project works, here is an example of a Spotify Wrapped image generated using the project:
In addition to analyzing users' top songs and artists, Spotify-Wrapped also offers a playlist analyzer feature. This feature analyzes a user's playlist and generates a chart based on their playlist.
To use Spotify-Wrapped, users need to install two Python libraries:
- Spotipy: A Python library used to interact with the Spotify Web API. This library can be installed using
pip install spotipy. - Pillow: A Python imaging library used to generate the summary image. This library can be installed using
pip install pillow. - Numpy: A Python library for numerical computing, providing powerful array and matrix operations for scientific computing tasks. This library can be installed using
pip install numpy. - Pandas: A popular data analysis library for Python, providing powerful data structures and tools for working with structured data such as CSV files, SQL databases, and more. This library can be installed using
pip install pandas.
Contributions to this project are welcome. If you find a bug or have an idea for a new feature, please create an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.