Sonify - Synthetic Music Service

This dataset is a synthetic data collection generated for a fictional music streaming platform, Sonify. It is designed to mimic real-world scenarios for analysis, visualization, and machine-learning tasks in the music streaming domain. Whether you’re looking to predict user behavior, analyze listening habits, or test recommendation algorithms, this dataset offers diverse opportunities for experimentation.

Key Features:

• User Profiles: Includes anonymized user demographics and account details.
• Music Catalog: Metadata for songs, albums, and artists, including genres, release years, and popularity scores.
• Listening Behavior: Simulated user activity data such as song plays, skips, repeats, and session durations.
• Engagement Metrics: Information on user interactions like likes, shares, and playlist additions.
• Subscription Details: Insights into free vs. premium account types and churn patterns.

Use Cases:

• Explore user engagement trends.
• Build recommendation systems using collaborative or content-based filtering.
• Analyze churn prediction for subscription models.
• Experiment with a time-series analysis of listening habits.

Notes:

• This dataset is fully synthetic and does not reflect real-world data.
• It is intended for educational and research purposes only.