Spotify Analytics Platform

A data engineering project analyzing 10+ years of personal streaming data (100K+ records) to extract behavioral insights through advanced temporal analysis and interactive visualizations.

🎯 Project Highlights

• Processes 100,000+ streaming records spanning 10+ years from Spotify's extended data export
• Built complete ETL pipeline with Python for multi-format JSON parsing and data enrichment
• Implements temporal pattern analysis revealing hourly, daily, seasonal, and yearly trends
• Features interactive Streamlit dashboard with real-time filtering and data export capabilities
• Maintains 100% data privacy with local-only processing

🛠 Technical Stack

Core: Python, Pandas, NumPy
Visualization: Streamlit, Plotly
Analytics: Scikit-learn (clustering, behavioral modeling)
Data: Parquet, JSON

📊 Key Features

Data Pipeline

• Multi-format parser handling music, podcasts, audiobooks, and video content
• Batch processing optimization for large-scale JSON datasets
• Derived metrics: skip rates, listening efficiency, session patterns
• Automated data enrichment and aggregation

Analytics Engine

• Temporal Analysis: Peak listening times, seasonal patterns, multi-year trends
• Behavioral Modeling: Skip patterns, session duration distributions, platform usage
• Preference Evolution: Artist and genre shifts over time
• Content Analysis: Format preferences (music vs. podcasts) and consumption patterns

Interactive Dashboard

• Real-time filtering across time ranges and content types
• Exportable visualizations and processed datasets
• Session analysis with efficiency metrics
• Platform usage breakdown (mobile, desktop, smart speakers)

🚀 Quick Start

# Clone and setup
git clone <repo-url>
cd spotify-analytics
python setup.py

# Add your Spotify data export to data/raw/
# Download from: spotify.com/account/privacy

# Run analysis pipeline
python main.py

# Launch dashboard
streamlit run src/visualization/interactive_dashboard.py

📁 Architecture

src/
├── data_processing/      # ETL pipeline & parsers
├── analysis/            # Pattern detection & modeling  
├── visualization/       # Dashboard components
└── config/             # Batch sizes & parameters

results/
├── enriched_data.parquet       # Processed dataset
├── temporal_patterns.json      # Time-based insights
└── session_behavior.json       # Behavioral metrics

💡 Sample Insights Generated

• Identified optimal productivity hours through listening pattern analysis
• Discovered 3 distinct listening "personas" through clustering
• Quantified attention span changes over 10-year period
• Revealed seasonal genre preferences and mood patterns

🔒 Privacy Note

This project exclusively analyzes my personal data, processed entirely locally. No external APIs or third-party services are used.

📈 Skills Demonstrated

• Data Engineering: Large-scale JSON processing, ETL pipeline design
• Analytics: Temporal analysis, behavioral modeling, pattern recognition
• Software Architecture: Modular design, configuration management
• Visualization: Interactive dashboards, real-time filtering