YouTube Music Metrics

Welcome to the YouTube Music Metrics project! This project aims to analyze and visualize your YouTube Music listening history data using the ytmusicapi library and Python.

Project Overview

The main goals of this project are:

  • Retrieve your YouTube Music listening history data using the ytmusicapi library
  • Analyze your listening patterns over time, such as hourly, daily, or weekly trends
  • Identify your most played songs, artists, or genres based on the listening history
  • Visualize your listening activity using line graphs or heatmaps to showcase temporal patterns
  • Document the findings, insights, and visualizations using mkdocs

Getting Started

To get started with this project, follow these steps:

  1. Clone the project repository from GitHub
  2. Create and activate the conda environment using the provided environment.yml file
  3. Obtain YouTube Music API credentials and add them to the auth.json file
  4. Launch Jupyter Notebook and open the listening_history_analysis.ipynb notebook
  5. Run the code cells in the notebook to perform the analysis

Project Structure

The project structure is as follows:

  • auth.json: Contains the YouTube Music API authentication credentials (not tracked by Git)
  • docs/: Contains the mkdocs documentation files
  • images/: Directory to store visualization images
  • index.md: The main page of the documentation (this file)
  • visuals.md: Showcases the visualizations created during the analysis
  • notebooks/: Contains the Jupyter Notebook files
  • listening_history_analysis.ipynb: The main notebook for analyzing the listening history data
  • .gitignore: Specifies files and directories to be ignored by Git
  • environment.yml: Defines the conda environment and dependencies
  • mkdocs.yml: Configuration file for mkdocs
  • README.md: Provides an overview of the project
  • requirements.txt: Lists the Python packages required for the project

Visualizations

Check out the Visualizations page to see the interesting insights and patterns discovered from your YouTube Music listening history data.