🎵 Spotify Wrapped AI

Project Structure

spotify-wrapped/
├── app.py                 # Main Streamlit story-flow app
├── requirements.txt       # Python dependencies
├── .gitignore
├── data/
│   └── SytheticData1000.csv    # Your Spotify listening data
├── src/
│   ├── data_pipeline.py   # Data loading & feature engineering
│   └── eda_analysis.py    # EDA visualizations
├── ml/
│   ├── user_classifier.py    # User personality classification
│   ├── mood_analyzer.py      # K-Means mood clustering
│   ├── genre_classifier.py   # Random Forest genre prediction
│   ├── taste_matcher.py      # ALS matrix factorization
│   ├── hybrid_recommender.py # Hybrid recommendation engine
│   └── model_trainer.py      # Model training pipeline
├── api/
│   └── fastapi_backend.py    # FastAPI REST API
└── docs/
    ├── IMPLEMENTATION_SUMMARY.md
    ├── PROJECT_STRUCTURE.md
    ├── QUICK_REFERENCE.md
    └── STREAMLIT_GUIDE.md

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Run the App

streamlit run app.py

3. Open in Browser

Navigate to http://localhost:8501

ML Models

Model	Purpose	Algorithm
TasteMatcher	User-track taste matching	ALS Matrix Factorization
MoodAnalyzer	Mood/vibe detection	K-Means Clustering
GenreClassifier	Genre prediction	Random Forest
UserClassifier	Personality types	Rule-based + K-Means
HybridRecommender	Track recommendations	CF + Content-Based

Tech Stack

• Frontend: Streamlit + Plotly
• Backend: FastAPI
• ML: scikit-learn, implicit
• Data: pandas, numpy

License

MIT License