https://github.com/ayushmaan-xd/exploratory-spotify-data-analysis

Exploratory Spotify Data Analysis is a project where I analyzed Spotify’s music dataset to uncover trends in audio features and song popularity. Using Python and data visualization tools
https://github.com/ayushmaan-xd/exploratory-spotify-data-analysis

jupyter-notebook matplotlib numpy pandas python seaborn spotify-dataset

Last synced: 10 months ago
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Exploratory Spotify Data Analysis is a project where I analyzed Spotify’s music dataset to uncover trends in audio features and song popularity. Using Python and data visualization tools

• Host: GitHub
• URL: https://github.com/ayushmaan-xd/exploratory-spotify-data-analysis
• Owner: Ayushmaan-XD
• Created: 2025-04-10T08:21:22.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-04-10T08:49:52.000Z (10 months ago)
• Last Synced: 2025-04-10T22:48:43.437Z (10 months ago)
• Topics: jupyter-notebook, matplotlib, numpy, pandas, python, seaborn, spotify-dataset
• Language: Jupyter Notebook
• Homepage:
• Size: 3.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# Exploratory-Spotify-Data-Analysis

![Logo](https://media-hosting.imagekit.io/87fd06c22ac34573/screenshot_1744274775303.png?Expires=1838882776&Key-Pair-Id=K2ZIVPTIP2VGHC&Signature=pA2yc361L1QdqsrEyHU2kSfHWp6X0PICuuETxNNyRFKN1hdAqIz1mNVmRvM8qMh20O6oGnr6N7PuIu3aBwUO45VzJgGzvvqXmKKGWXDhVadBktvDDcxGAjYZQEYSyD-hfw4ZTuKdCt9C5jMCNhsDmt1XEWfR9MabH4OONJnQ6U8x~q0kQIp0gfeTwHQLnUr4QAhtEkL7zBwqC1cA2YI0Ra4y~-pV2W18dRAxGKuUArwvCVZX4Z~O9JuFnAe5HQRt8mA7opl-fDQHncdhRqgEmFMwwRb8mX9Zk7vgfZYHO7VC1QrcmaQId7Q6fVNebvgEEqPWSfMCYuUpG25FtzpJtQ__)

This project involves an in-depth exploratory data analysis (EDA) of Spotify's music dataset to uncover patterns, trends, and insights into user preferences and song characteristics. The primary goal is to understand the relationships between different audio features and how they influence a song's popularity.

## Key Objectives:

 - Analyze key audio features such as Rock, Folk music, Disco, pop, jazz, hip hop, Rythem.

 - Identify trends in music preferences over time.

 - Understand what makes a song popular on Spotify.

 - Visualize distributions, correlations, and feature comparisons using tools like Matplotlib, Seaborn, and Plotly.

 - Perform genre analysis to see how audio features vary across genres.

 - Explore artist-wise performance and insights from top tracks.

## Outcomes:

 - Built interactive and static visualizations to reveal patterns in musical taste.

 - Generated insights into what attributes are common among popular songs.

 - Helped in understanding how audio features impact the popularity score.

 - Created a foundation for further work like music recommendation systems or trend prediction.