Exploratory Report of Top 10,000 Spotify Songs (1950–2024)

This project presents an exploratory data analysis of the top 10,000 Spotify tracks from 1950 to 2024, highlighting how popular music has evolved over the decades.

Using data visualization, feature engineering, and classification techniques, the analysis explores:

• Audio attributes (e.g., danceability, energy, acousticness)
• Genre prevalence and diversity
• Mood classifications
• Popularity trends

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Dataset

• Source: Kaggle – Top 10,000 Spotify Songs
• Author: Joakim Arvidsson
• Years Covered: 1950–2024
• Format: CSV (includes song metadata, audio features, genres, and popularity)

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Tools & Technologies

• Python
• Pandas, NumPy
• Seaborn, Matplotlib, Plotly
• Scikit-learn (for mood classification)
• Jupyter Notebook

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Project Highlights

• Cleaned and transformed noisy or incomplete data entries
• Classified tracks by mood using audio features
• Visualized long-term shifts in genre dominance and listening trends
• Tracked popularity metrics to reveal the rise of streaming-driven hits

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License

This project is released under the MIT License. See the LICENSE file for details.

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Author

Joakim Arvidsson
Original dataset creator on Kaggle

Project by: Adeniya Fatimo Adenike

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