Project Overview
In collaboration with Hudson Griffith, we developed a comprehensive analysis of Spotify's Wrapped feature, creating interactive tools to explore music listening patterns and preferences. The project combines technical analysis with user-friendly visualization to provide insights into how Spotify processes and presents user listening data.
Key Features
- Interactive state-by-state music preference mapping
- "Basicness" calculator for comparing music taste to regional trends
- Detailed analysis of Spotify's data collection and processing methods
- Integration with OpenAI's GPT for dynamic user feedback
Technical Implementation
- Built using Python and FastAPI for backend processing
- Implemented data parsing algorithms for user preference analysis
- Created interactive visualizations for data presentation
- Integrated machine learning models for preference analysis
Links
Project Details
The Spotify Wrapped Analysis project dives deep into understanding how Spotify processes and presents user listening data. We reverse-engineered Spotify's calculation methods and created tools to help users better understand their music preferences.
Research Methodology
Our analysis involved:
- Analyzing extended streaming data from multiple users
- Testing various date ranges and parameters against known Wrapped results
- Implementing Python-based data analysis tools
- Creating visualization tools for presenting findings