Spotify_Data_Analytics
RoleData Analytics
Year2026
Project Details
The Spotify dataset allowed us to explore music trends, song characteristics, and listener behaviour over time. Features such as danceability, energy, tempo, and loudness show clear variation across songs and help define a track's style. Certain genres and artists appear far more frequently, indicating higher listener preference and greater representation in the Spotify catalog. Song popularity changes significantly across years, reflecting evolving music trends and shifts in audience taste, especially in the digital streaming era.
Skills
Data AnalyticsData WranglingInsight GenerationMarket Trend AnalysisData VisualizationContent Analysis
Tools
PythonPandasNumPySeabornMatplotlib
This project focuses on analyzing Spotify music data to understand trends and patterns in audio features, artist popularity, genre preferences, and song success over time.
We will:
- Load multiple Spotify datasets
- Clean and preprocess the data
- Explore distributions of key features (energy, tempo, loudness, danceability, etc.)
- Examine correlations between attributes
- Analyze trends in genre, artist, and yearly popularity
- Build predictive models to estimate song popularity
- Compare model performance to find the most effective model.
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