Description
This project explores music popularity dynamics through Shazam-related signals over time. The objective was to move from static rankings to temporal understanding: trend acceleration, periodic effects, anomalies, and momentum phases.
Key Features
- Temporal Exploration: Built a full time-series pipeline to inspect trend evolution instead of one-shot snapshots.
- Seasonality & Patterns: Identified recurring patterns and shifts in listening behavior.
- Forecasting Logic: Evaluated predictive baselines to estimate short-term movement.
- Insight Visualization: Produced clear visual narratives to communicate trend changes and signal quality.
Technologies Used
- Python (Pandas, NumPy)
- Time-series tooling and statistical methods
- Matplotlib / Seaborn
- Data preprocessing and feature engineering
Roles and Contributions
I designed and implemented the analysis pipeline end to end: data preparation, feature building, temporal diagnostics, model experimentation, and visualization storytelling.
Outcome
The project provided a robust framework to interpret Shazam dynamics over time, helping transform raw events into actionable insights on music trajectory and trend behavior.