YouTube Analytics Dashboard

A Streamlit-powered dashboard analyzing top YouTube channels, their performance metrics, and trends. This project implements various data visualizations and machine learning models to derive insights from YouTube channel data.

Preview

Features

• Top 100 YouTube channel category distribution
• Predictive analysis of likes vs. subscribers relationship
• Global YouTuber distribution
• Annual view trends for top channels
• Revenue analysis
• Channel clustering based on performance metrics
• Category-wise follower analysis
• Top performing channels visualization

Technologies Used

• Python 3.x
• Streamlit
• Pandas & NumPy
• Scikit-learn
• Plotly
• Seaborn
• Matplotlib

Installation

git clone https://github.com/cam-cc/youtube-analytics.git
cd youtube-analytics
pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

Project Structure

├── app.py              # Main Streamlit application
├── data/               # Data files
│   ├── top_100_youtubers.csv
│   └── avg_view_every_year.csv
├── src/                # Source code
│   ├── preprocessing/  # Data preprocessing scripts
│   ├── models/         # ML models
│   └── visualization/  # Visualization functions
│       └── components/ # Analyzers per chart
└── requirements.txt    # Project dependencies

License

This project is part of an academic assignment and is available for educational purposes | Fanshawe

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Open a Pull Request