This project presents a comprehensive exploratory data analysis and transformation pipeline applied to a multi-platform music dataset. The dataset encompasses detailed information about songs and their performance metrics across major streaming platforms including Spotify, YouTube, and TikTok, providing a holistic view of modern music consumption patterns.
In today's digital music landscape, understanding cross-platform performance is crucial for artists, labels, and streaming services. This project bridges that gap by analyzing the complex relationships between various performance indicators and identifying the key factors that drive musical success across different platforms.
Key Research Questions:
- How do Spotify popularity scores correlate with YouTube engagement metrics?
- What role does TikTok virality play in overall song success?
- Are there temporal patterns in music popularity across different release years?
- Which audio features most strongly predict cross-platform success?
Data Pipeline Components:
- Data Cleaning & Preprocessing: Handled missing values, outlier detection, and data type conversions to ensure data integrity
- Feature Engineering: Created derived metrics and normalized scores for cross-platform comparison
- Exploratory Data Analysis (EDA): Employed advanced visualization techniques including:
- Correlation heatmaps for feature relationship mapping
- Distribution analysis using violin plots and box plots
- Temporal trend analysis with time series visualizations
- Scatter plot matrices for multi-dimensional pattern recognition
- Data Transformation: Applied scaling, encoding, and statistical transformations to optimize data for downstream analysis
The analytical findings provide actionable intelligence for:
- Music Streaming Platforms: Understanding user engagement patterns and optimizing recommendation algorithms
- Record Labels: Identifying promising tracks and developing targeted marketing strategies
- Artists & Producers: Gaining insights into audience preferences and platform-specific content optimization
- Music Industry Analysts: Benchmarking performance and predicting emerging trends
- Python for data processing and analysis
- Pandas & NumPy for data manipulation and statistical operations
- Matplotlib & Seaborn for comprehensive data visualization
- Statistical Analysis for pattern identification and trend analysis
This project demonstrates the power of data-driven decision making in the music industry. The methodologies and insights developed here can be extended to:
- Real-time performance tracking dashboards
- Predictive modeling for song success
- A/B testing frameworks for marketing campaigns
- Integration with machine learning models for personalized recommendations
The comprehensive data transformation pipeline established in this project serves as a foundation for more advanced analytics and machine learning applications in the music streaming domain.