📺 YouTube Studio Data Analytics - Modular Edition

A comprehensive, modular Python application for analyzing YouTube Studio data with interactive visualizations, machine learning predictions, and automated reporting.

🚀 Features

• 📊 Interactive Dashboards: Web-based dashboards using Streamlit and Dash
• 📈 Time Series Analysis: Views, likes, and comments trends over time
• 🎯 Engagement Analytics: Like rates, comment rates, and engagement patterns
• 👥 Subscriber Insights: Growth and decline analysis with detailed metrics
• 🤖 ML Predictions: Machine learning models to predict video performance
• 📁 Export Reports: Automated Excel, JSON, and HTML report generation
• 🔥 Correlation Analysis: Performance correlation heatmaps
• 📱 Responsive Design: Works on desktop and mobile devices
• 🏗️ Modular Architecture: Professional code structure for maintainability

📋 Project Structure

YouTubeStudioDataAnalytics/
├── 🎯 main.py                       # Main entry point with CLI
├── � src/                          # Source code modules
│   ├── 📊 analytics/                # Core analytics package
│   │   ├── __init__.py             # Package initialization
│   │   ├── core.py                 # Main YouTubeAnalytics class
│   │   ├── data_loader.py          # Data loading and preprocessing
│   │   ├── visualizations.py      # Chart generation with Plotly
│   │   └── ml_predictor.py         # Machine learning models
│   ├── 🌐 dashboards/              # Dashboard implementations
│   │   ├── __init__.py             # Dashboard package init
│   │   ├── streamlit_app.py        # Streamlit dashboard
│   │   └── dash_app.py             # Dash dashboard
│   └── �️ utils/                   # Utility modules
│       ├── __init__.py             # Utils package init
│       ├── config.py               # Configuration management
│       ├── data_utils.py           # Data validation & statistics
│       └── export_utils.py         # Export and reporting utilities
├── 📁 data/                         # Data directory
│   ├── sample/                     # Sample data files
│   │   ├── videos.csv              # Sample video data
│   │   └── subscribers.csv         # Sample subscriber data
│   └── exports/                    # Generated reports and charts
├── ⚙️ config/                      # Configuration files
│   └── config.json                 # Default configuration
├── 🧪 tests/                       # Test suite
│   ├── __init__.py                 # Test package init
│   └── test_imports.py             # Import tests
├── 📓 notebooks/                   # Jupyter notebooks
├── 📋 docs/                        # Documentation
├── 📦 requirements.txt             # Python dependencies
└── 📖 README.md                    # This file

🛠️ Quick Setup

Installation

# Clone the repository
git clone https://github.com/DuongCaoNhan/YouTubeStudioDataAnalytics.git
cd YouTubeStudioDataAnalytics

# Install dependencies
pip install -r requirements.txt

# Or install individually
pip install pandas plotly streamlit dash scikit-learn openpyxl jupyter

📊 Getting Your YouTube Data

From YouTube Studio:

1. Go to YouTube Studio → Analytics
2. Click "Export Report" tab
3. Select date range and metrics
4. Export as CSV files:
   • videos.csv: Video performance data
   • subscribers.csv: Subscriber activity data

Required CSV Columns:

videos.csv:

Title, Publish Date, Views, Likes, Comments, Duration (minutes)

subscribers.csv:

Date, Subscribers Gained, Subscribers Lost, Net Subscribers

🚀 Usage Options

1. 🎯 Command Line Interface (NEW!)

# Run complete analytics pipeline
python main.py --analysis

# Start interactive Streamlit dashboard
python main.py --streamlit

# Start professional Dash dashboard  
python main.py --dash

# Quick data analysis only
python main.py --data-only

# ML prediction demonstration
python main.py --ml-demo

# Interactive mode (default)
python main.py

2. 📊 Programmatic Usage

from src.analytics import YouTubeAnalytics

# Initialize with your data
analytics = YouTubeAnalytics(
    videos_file="data/sample/videos.csv",
    subscribers_file="data/sample/subscribers.csv"
)

# Run complete analysis
results = analytics.run_complete_analysis()

# Generate specific visualizations
charts = analytics.create_all_visualizations()

# Train ML models
ml_results = analytics.train_prediction_model()

# Export results
analytics.export_results("output/")

4. 📁 Generated Outputs

After running analysis, you'll find:

data/exports/
├── 📊 youtube_analytics_report.xlsx    # Excel report with multiple sheets
├── 📈 charts/                         # Interactive HTML charts
│   ├── views_timeline.html
│   ├── engagement_comparison.html
│   ├── correlation_heatmap.html
│   └── ... (10 chart files)
├── 🤖 ml_model.joblib                 # Trained ML model
├── 📄 analysis_results.json           # Complete results in JSON
├── 📋 processed_videos.csv            # Processed video data
└── 📋 processed_subscribers.csv       # Processed subscriber data

5. 🔧 Configuration

Customize behavior via config/config.json:

{
  "ml": {
    "default_model_type": "linear",
    "hyperparameter_tuning": true
  },
  "visualization": {
    "default_theme": "plotly_white",
    "color_palette": ["#1f77b4", "#ff7f0e", "..."]
  }
}

Features:

• Complete analytics pipeline
• Interactive Plotly charts
• Summary statistics
• ML predictions
• Excel report export

2. 🌐 Streamlit Web Dashboard

streamlit run streamlit_dashboard.py

Then open: http://localhost:8501

Features:

• Real-time interactive dashboard
• Multi-page navigation
• Date range filtering
• Live data updates
• Mobile-responsive design

3. 🌐 Dash Web Dashboard

python dash_dashboard.py

Then open: http://localhost:8050

Features:

• Professional dashboard interface
• Advanced interactivity
• Custom styling
• Real-time updates

4. 📓 Jupyter Notebook Analysis

jupyter notebook youtube_analytics_notebook.ipynb

Features:

• Step-by-step analysis
• Detailed explanations
• Interactive exploration
• Customizable analysis

📈 Key Analytics Features

📊 Video Performance Metrics

• Views Analysis: Track view patterns over time
• Engagement Rates: Like rate and comment rate calculations
• Performance Comparison: Compare videos side-by-side
• Top Performers: Identify your best-performing content

👥 Subscriber Analytics

• Growth Tracking: Monitor subscriber gains and losses
• Activity Patterns: Analyze subscriber behavior over time
• Net Growth: Calculate overall subscriber growth trends

🤖 Machine Learning Predictions

• View Prediction: Predict views for new videos
• Feature Importance: Understand what drives performance
• Model Accuracy: R² scores and performance metrics
• Interactive Predictor: Test different video parameters

📊 Visualization Types

• Line Charts: Time series trends
• Bar Charts: Performance comparisons
• Scatter Plots: Correlation analysis
• Heatmaps: Performance correlation matrices
• Histograms: Distribution analysis
• Bubble Charts: Multi-dimensional analysis

🎯 Sample Insights You'll Get

📈 Performance Insights

• Which video topics perform best
• Optimal video length for your audience
• Best publishing times and patterns
• Engagement rate benchmarks

🎬 Content Strategy

• Content types that drive most engagement
• Correlation between video length and performance
• Subscriber impact of different content types
• Seasonal performance patterns

📊 Predictive Analytics

• Expected performance for new videos
• Feature importance for video success
• Performance confidence intervals
• Optimization recommendations

⚙️ Configuration

Edit config.ini to customize:

[data_files]
videos_file = your_videos.csv
subscribers_file = your_subscribers.csv

[dashboard]
streamlit_port = 8501
dash_port = 8050

[visualization]
youtube_red = #FF0000
chart_height = 500

📁 Export Options

Excel Reports

• Comprehensive analytics data
• Summary statistics
• Top performers lists
• Subscriber activity data

PDF Reports (Coming Soon)

• Executive summary
• Key visualizations
• Performance insights
• Recommendations

🔧 Advanced Features

🎮 Interactive Prediction Tool

Test how different parameters affect predicted views:

• Video duration
• Expected engagement rates
• Content type factors

📊 Custom Metrics

• Engagement Rate = (Likes + Comments) / Views × 100
• Like Rate = Likes / Views × 100
• Comment Rate = Comments / Views × 100
• Performance Score = Combined weighted metrics

🔍 Correlation Analysis

• Identify relationships between metrics
• Understand performance drivers
• Optimize content strategy

🛠️ Troubleshooting

Common Issues:

1. Import Errors

pip install --upgrade -r requirements.txt

2. Data Loading Issues

• Check CSV file format and column names
• Ensure dates are in YYYY-MM-DD format
• Verify file paths in config.ini

3. Dashboard Not Loading

• Check if ports 8501/8050 are available
• Try different ports in config.ini
• Restart the dashboard application

4. Chart Display Issues

• Update your browser
• Clear browser cache
• Check Plotly version: pip install --upgrade plotly

📚 Dependencies

Core Libraries:

• pandas: Data manipulation and analysis
• plotly: Interactive visualizations
• numpy: Numerical computations
• scikit-learn: Machine learning models

Dashboard Libraries:

• streamlit: Web dashboard framework
• dash: Professional dashboard framework

Export Libraries:

• openpyxl: Excel file generation
• reportlab: PDF report generation

🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• Issues: Report bugs or request features on GitHub
• Documentation: Check the wiki for detailed guides
• Community: Join discussions in the Issues section

🔮 Roadmap

Upcoming Features:

• Real-time API Integration with YouTube Data API
• Advanced ML Models (Random Forest, Neural Networks)
• Automated Insights with AI-generated recommendations
• Team Collaboration features for multiple users
• Mobile App for on-the-go analytics
• Custom Alerts for performance thresholds
• A/B Testing Tools for content optimization
• Competitive Analysis features

📞 Contact

• GitHub: @DuongCaoNhan
• Email: [email protected]
• LinkedIn: Duong Cao Nhan

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🚀 Happy analyzing your YouTube data! 📺✨