Music Data Visualization Project

This project provides an exploratory data analysis and visualization of a music dataset (songs_normalize.csv). The main goals are to understand the relationships between musical features, popularity, and trends over time.

Features

• Data Cleaning & Preparation:
  • Handles missing values and normalizes numeric columns.
  • Simplifies multi-genre entries to a single genre.
• Visualizations:
  • Danceability vs. Energy Scatter Plot:
    • Shows the relationship between danceability and energy, colored by year (with a clear colorbar and dot markers).
  • Popularity Histogram:
    • Displays the distribution of song popularity scores.
  • Average Popularity by Genre Bar Plot:
    • Compares average popularity across different genres.

Usage

• Open Music_Visualization.ipynb in Jupyter or VS Code.
• Run the cells in order to load the data, clean it, and generate the visualizations.
• Output images are saved as PNG files in the project directory.

Files

• Music_Visualization.ipynb: Main analysis and visualization notebook.
• songs_normalize.csv: The dataset used for analysis.
• danceability_vs_energy_refined.png: Scatter plot output.
• popularity_histogram_refined.png: Popularity histogram output.
• popularity_by_genre_refined.png: Bar plot of average popularity by genre.

Requirements

• Python 3.x
• pandas
• matplotlib
• seaborn
• numpy

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Feel free to extend this project with more visualizations or deeper analysis!