YouTube Watch History Analysis

This Jupyter notebook analyzes your YouTube watch history to provide insights about your viewing patterns and interests. The notebook processes your watch history data from a JSON file and uses OpenAI's GPT model to generate personalized insights.

Features

• Filters and processes YouTube watch history from the last year
• Removes duplicate entries and short-form content
• Identifies most frequently rewatched videos
• Generates AI-powered insights about:
  • Your personality and habits
  • Passions and interests
  • Notable patterns in your viewing history
  • Values and focus areas
• Provides personalized recommendations for future learning and content consumption

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required Python packages:
  • json
  • datetime
  • python-dateutil
  • python-dotenv
  • openai
  • IPython

Setup

1. Export your YouTube watch history as a JSON file from Google Takeout
2. Place the watch-history.json file in the same directory as the notebook
3. Create a .env file with your OpenAI API key:

   OPENAPI_KEY=your_api_key_here
   

Usage

1. Run the notebook cells in sequence
2. The notebook will:
   • Process your watch history
   • Generate statistics about your viewing patterns
   • Use OpenAI to analyze your viewing habits
   • Provide recommendations for future content consumption

Output

• Total and unique video counts
• List of most frequently rewatched videos
• AI-generated analysis of your viewing patterns
• Personalized recommendations for future learning

Note

The notebook filters out:

• Videos watched more than a year ago
• Short-form content
• Videos with emoji in titles
• Videos watched within 1 minute of each other
• URLs and hashtag content