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SPOTIFY DATA ANALYSIS PROJECT - CURRENT STATUS

Internal Progress Document for Copilot Sessions

Last Updated: August 4, 2025, 21:12 Project Phase: Multi-Source Metadata Enrichment (In Progress)

🎯 PROJECT OVERVIEW

Goal: Analyze 15 years of personal Spotify listening history with comprehensive metadata enrichment Dataset: 138,761 streaming records, 27,400 unique tracks Approach: Multi-source enrichment strategy (External datasets + Spotify API)

📊 CURRENT STATUS SUMMARY

Data Coverage (As of 2025-08-04 21:12)

  • Total Records: 138,761 streaming records
  • Unique Tracks: 27,400 tracks
  • Current Combined Coverage: 52.5% of records (72,897 enriched)
  • Unique Track Coverage: 40.6% (11,120 tracks with metadata)

Coverage Breakdown by Source:

  1. Kaggle/External Dataset:

    • Records: 42,473 (30.6%)
    • Unique tracks: 6,130 (22.4%)
    • Status: ✅ Complete

  2. Spotify API:

    • Records: 38,524 (27.8%)
    • Unique tracks: 4,990 (18.2%)
    • Status: 🔄 Session 1 complete, 3-4 more sessions needed

  3. Smart Overlap: 0 tracks (perfect filtering working!)

Remaining Work:

  • ~16,280 unique tracks still need metadata (~59.4%)
  • API Progress: 5,500 tracks processed, ~15,700 remaining
  • Next API Session: Available after cooldown (~22 hours from last session)

🗂️ FILE STRUCTURE & LOCATIONS

spotify-data/
├── data/
│   ├── processed/
│   │   └── cleaned_streaming_history.csv          # Base cleaned dataset
│   └── enriched/
│       ├── ultimate_spotify_enriched_streaming_history.csv    # Kaggle enriched (✅ Complete)
│       ├── spotify_api_enriched_streaming_history.csv         # API enriched (🔄 Partial)
│       ├── spotify_api_metadata.csv                          # API metadata only
│       └── api_metadata_progress.pkl                         # API progress save file
├── scripts/
│   ├── spotify_api/
│   │   └── smart_metadata_enrichment.py          # Smart API enrichment (avoids duplicates)
│   ├── external_matching/
│   │   └── ultimate_spotify_matcher.py           # Kaggle dataset matching (completed)
│   ├── data_processing/
│   │   ├── combine-history.py                    # Combine JSON files (completed)
│   │   └── clean-history.py                      # Clean data (completed)
│   └── analysis/
│       └── coverage_analysis.py                  # Current coverage analysis
└── external_datasets/                            # Downloaded Kaggle datasets

🔧 TECHNICAL DETAILS

Spotify API Configuration:

  • Rate Limit: 10,000 calls per session (~70k second cooldown)
  • Current Status: Hit rate limit after 5,500 tracks (Session 1 complete)
  • Next Available: ~22 hours from last session end
  • Progress File: data/enriched/api_metadata_progress.pkl (auto-resume capability)

Smart Filtering System:

  • ✅ Working perfectly: 0 overlap between Kaggle and API sources
  • Avoids duplicate API calls on already-enriched tracks
  • Saved ~6,130 API calls in Session 1

Data Quality:

  • Kaggle Source: Audio features, genres, popularity, track info
  • API Source: Official IDs, detailed artist info, album metadata, additional genres
  • Success Rates: Kaggle ~22% match, API ~90% success rate

⚡ QUICK START COMMANDS

Check Current Coverage:

cd /home/usher/Documents/code/spotify-data
/home/usher/Documents/code/spotify-data/.venv/bin/python scripts/analysis/coverage_analysis.py

Resume API Enrichment (when cooldown ends):

cd /home/usher/Documents/code/spotify-data
/home/usher/Documents/code/spotify-data/.venv/bin/python scripts/spotify_api/smart_metadata_enrichment.py

Environment Setup:

  • Python Environment: /home/usher/Documents/code/spotify-data/.venv/bin/python
  • Required packages: pandas, spotipy, kagglehub, tqdm, python-dotenv
  • Spotify API credentials: Set in .env file (already configured)

🎵 AVAILABLE METADATA

From Kaggle/External (Complete):

  • Audio features: acousticness, danceability, energy, valence, tempo, etc.
  • Genre classifications
  • Track popularity and basic track information
  • Artist names and track IDs

From Spotify API (Partial - 18.2% complete):

  • Official Spotify track/artist IDs and URIs
  • Track popularity and release dates
  • Artist popularity and follower counts
  • Detailed genre information from artist profiles
  • Complete album metadata
  • Note: Audio features API endpoint has 403 errors, skipped

🚀 NEXT STEPS PRIORITY

  1. Wait for API cooldown (~22 hours from last session)
  2. Resume API enrichment - will auto-continue from batch 111/426
  3. Complete remaining 3-4 API sessions over next few days
  4. Final expected coverage: 70-80% when all sessions complete
  5. Analysis phase: Create visualizations and insights once enrichment complete

💡 KEY INSIGHTS FOR NEW SESSIONS

  • Smart filtering works perfectly - no wasted API calls
  • High API success rate (90%+) on tracks not in Kaggle dataset
  • Combined approach optimal - Kaggle for speed, API for completeness
  • Progress automatically saved - safe to interrupt and resume
  • Final coverage target: 70-80% achievable with current approach

🔍 TROUBLESHOOTING NOTES

  • Rate limits: Expected every ~5,000 tracks, plan for multi-day completion
  • Authentication: Spotify OAuth working correctly for user "Focuszero"
  • File paths: All scripts use proper paths for organized structure
  • Memory: Large CSV files, use dtype warnings are normal
  • Resume capability: API enrichment will continue from saved progress

📋 STATUS: Ready for next API session when cooldown expires. Project is on track for excellent final coverage!