spotify-swipe-based-recommendation-system

Phase 1: Research and EDA

Objectives:
- Gather data on user preferences and behavior patterns in music applications.
- Define project requirements and technical specifications + EDA.
Deliverables:
- Project plan document.
- Technical requirements and architecture.
- Initial data collection and analysis report.

Phase 2: Development of Song Popularity Prediction Model

Objectives:
- Develop a machine learning model to predict song popularity based on Spotify’s data such as play counts, user interaction, and metadata.
- Validate the model with historical data and adjust based on performance.
Deliverables:
- Machine learning model trained and tested.
- Documentation on model performance and metrics.

Phase 3: Implementation of the Recommendation System

Objectives:
- Build the backend for the recommendation system integrating the song popularity model.
- Implement algorithms for mood-based matchmaking, genre roulette, etc.
- Start with basic swipe interactions and feedback mechanisms.
Deliverables:
- Recommendation system backend.
- Initial version of the swipe interface for internal testing.

Phase 4: App or Web Extension Development

Objectives:
- Decide whether to develop a standalone app or a web extension based on ease of integration with Spotify and user accessibility.
- Develop a swipe-based interface for the app or web extension.
- Incorporate user feedback mechanisms into the interface.
Deliverables:
- Beta version of the app or web extension.