Spotify API Analysis Project: Track Popularity vs Duration

A hands-on project focused on API integration, data analysis, and statistical modeling using real-world music data from Spotify. This project demonstrates the end-to-end process of connecting to external APIs, extracting meaningful insights, and performing statistical analysis to understand relationships between track characteristics.

This project explores the relationship between track duration and popularity using Spotify's Web API. Through statistical analysis and data visualization, we investigate whether there's a correlation between how long a song is and how popular it becomes with listeners.

Key topics covered include:

• API authentication and data retrieval using the Spotify Web API
• Statistical analysis with ordinary least squares (OLS) regression
• Data visualization and interpretation
• Working with real-world music streaming data
• Best practices for API integration and data analysis

See instructions in the INSTRUCTIONS.md file for original assignment description.

1. Go to the Spotify Developer Dashboard
2. Create a new app to get your Client ID and Client Secret

1. Fork the Repository

   • Click the "Fork" button on the top right of the GitHub repository page
   • 4Geeks students: set 4GeeksAcademy as the owner - 4Geeks pays for your codespace usage. All others, set yourself as the owner
   • Give the fork a descriptive name. 4Geeks students: I recommend including your GitHub username to help in finding the fork if you loose the link
   • Click "Create fork"
   • 4Geeks students: bookmark or otherwise save the link to your fork

2. Set up your Spotify API credentials using Codespace secrets:

   • Go to your GitHub repository settings
   • Navigate to "Secrets and variables" → "Codespaces"
   • Add two repository secrets:
     • CLIENT_ID: Your Spotify client ID
     • CLIENT_SECRET: Your Spotify client secret
   • These will automatically be available as environment variables in your Codespace

3. Create a GitHub Codespace

   • On your forked repository, click the "Code" button
   • Select "Create codespace on main"
   • If the "Create codespace on main" option is grayed out - go to your codespaces list from the three-bar menu at the upper left and delete an old codespace
   • Wait for the environment to load (dependencies are pre-installed)

4. Start Working

   • Write your code in src/assignment.py
   • Run your code with the command python src/assignment.py

GitHub Codespaces provides a complete VS Code environment in your browser with all required extensions and packages pre-installed.

1. Prerequisites

   • Git
   • Python >= 3.10

2. Fork the repository

   • Click the "Fork" button on the top right of the GitHub repository page
   • Optional: give the fork a new name and/or description
   • Click "Create fork"

3. Clone the repository

   • From your fork of the repository, click the green "Code" button at the upper right
   • From the "Local" tab, select HTTPS and copy the link
   • Run the following commands on your machine, replacing <LINK> and <REPO_NAME>

   git clone <LINK>
   cd <REPO_NAME>

4. Set Up Environment

   • Create virtual environment

   python -m venv .venv

   • Set your Spotify API credentials as environment variables in the virtual environment by adding the following in .venv/bin/activate:

   export CLIENT_ID="your_spotify_client_id"
   export CLIENT_SECRET="your_spotify_client_secret"

   • Activate the virtual environment & install dependencies:

   source .venv/bin/activate
   pip install -r requirements.txt

5. Start Working

   • Write your code in src/assignment.py
   • Run your code with the command python src/assignment.py

├── .devcontainer/          # Codespace/development container configuration
├── assets/                 # Images and other files
│
├── src/                    # Source code
│   └── assignment.py       # Your code goes here
│   └── solution.py         # Reference solution
│
├── .gitignore              # Files/directories not tracked by git
├── requirements.txt        # Python dependencies
├── INSTRUCTIONS.md         # Assignment instructions
└── README.md               # Project documentation

• Connect to external APIs using proper authentication methods
• Extract and process JSON data from REST API endpoints
• Handle API responses and error cases gracefully

• Perform regression analysis to identify relationships between variables
• Create meaningful visualizations to communicate findings
• Interpret statistical results and p-values

Example insights you might discover:

• Whether longer songs tend to be more or less popular
• The strength of the relationship between duration and popularity
• Statistical significance of any observed patterns

• Python 3.11: Core programming language
• Spotipy: Python library for Spotify Web API
• NumPy: Numerical computing and array operations
• Matplotlib: Data visualization and plotting
• Statsmodels: Statistical modeling and regression analysis
• Spotify Web API: Music streaming data and metadata

This project is designed for educational purposes. Contributions to improve the analysis, add new features, or enhance explanations are welcome:

1. Fork the repository
2. Create a feature branch
3. Make your improvements
4. Submit a pull request