https://github.com/akku-1206/spotify_data_analysis
Spotify Tracks Analysis Overview This project performs an exploratory data analysis (EDA) on Spotify music data to uncover insights and correlations related to song features, popularity, and genre.
https://github.com/akku-1206/spotify_data_analysis
matplotlib numpy pandas python seaborn
Last synced: 2 months ago
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Spotify Tracks Analysis Overview This project performs an exploratory data analysis (EDA) on Spotify music data to uncover insights and correlations related to song features, popularity, and genre.
- Host: GitHub
- URL: https://github.com/akku-1206/spotify_data_analysis
- Owner: Akku-1206
- Created: 2024-11-08T08:32:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-08T13:28:25.000Z (over 1 year ago)
- Last Synced: 2025-07-24T08:40:43.030Z (11 months ago)
- Topics: matplotlib, numpy, pandas, python, seaborn
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Spotify Tracks Analysis
This project performs an exploratory data analysis (EDA) on Spotify music data to uncover insights and correlations related to song features, popularity, and genre. Using Python and popular data science libraries like Pandas, Seaborn, and Matplotlib, the project provides data cleaning, manipulation, and visualization to help identify patterns and trends in music data.
## Objectives
The main objectives of this analysis are:
- Identifying popular songs and genres.
- Understanding correlations between different song attributes.
- Analyzing trends over time, including song duration and release years.
## Requirements
To run this project, you'll need:
- **Python** 3.x
- Libraries: `numpy`, `pandas`, `matplotlib`, `seaborn`
To install the required libraries, use:
```bash
pip install numpy pandas matplotlib seaborn