⛏️ Mining Data Dashboard

A comprehensive Streamlit-based dashboard for analyzing mining operations data, including stope widths, blast sequencing, and equipment utilization.

📸 Dashboard Screenshots

Main Dashboard Overview

Core Analysis Sections

Stope Width Analysis	Equipment Utilization

Blast Sequencing	Equipment Performance

Technical Analysis

Distribution Analysis	Zone Analysis

Individual Stope Tracking	Multi-Stope Comparison

🎯 Key Insights from Sample Data

Based on the generated mining data, this dashboard reveals:

• Average stope width: 15.23m (typical for bulk mining operations)
• Equipment utilization: 66.9% (good operational efficiency)
• Width variation: 19.9% (moderate geological consistency)
• Zone performance: Comparative analysis across North, South, East, West zones

📊 Features

1. Stope Width Analysis

• Real-time monitoring of stope width variations
• Zone-wise analysis and comparisons
• Statistical analysis and trend identification
• Distribution analysis across different mining zones

2. Blast Sequencing Analysis

• Explosives usage tracking and optimization
• Fragmentation size analysis
• Vibration level monitoring
• Sequence performance evaluation

3. Equipment Utilization

• Utilization rate monitoring across equipment types
• Maintenance and downtime analysis
• Fuel consumption tracking
• Performance metrics by equipment ID

🛠️ Tech Stack

• Python 3.8+
• Pandas - Data manipulation and analysis
• Streamlit - Web application framework
• Plotly - Interactive visualizations
• NumPy - Numerical computations

🚀 Installation

1. Clone the repository:

git clone https://github.com/Axulo-Inc/mining-data-dashboard.git
cd mining-data-dashboard

2. Install required packages:
pip install -r requirements.txt

3. Run the application:
streamlit run app.py

## 📁 Project Structure

\`\`\`
mining-data-dashboard/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
├── screenshots/          # Dashboard screenshots
├── utils/
│   ├── data_loader.py    # Data generation and loading
│   └── calculations.py   # Statistical calculations
└── components/
    ├── stope_analysis.py      # Stope width analysis components
    ├── blast_sequencing.py    # Blast sequencing components
    └── equipment_utilization.py # Equipment utilization components
\`\`\`

## 🎯 Usage

1. **Navigation**: Use the sidebar to switch between different analysis types
2. **Date Filtering**: Filter data by date range using the sidebar controls
3. **Interactive Charts**: Hover over charts for detailed information
4. **Real-time Metrics**: View key performance indicators in the sidebar

## 📈 Data Sources

The dashboard supports both:
- Sample Data: Automatically generated realistic mining data
- Real CSV Data: Import your own mining operation data by placing CSV files in the data/ directory

## 🔧 Customization

To use with your own data:
1. Replace the data generation functions in \`utils/data_loader.py\`
2. Modify the data processing in component files
3. Update visualization parameters as needed

## 🤝 Contributing

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

## 📄 License

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

## 👥 Authors

- **Thabang Motsoahae** - [Axulo-Inc](https://github.com/Axulo-Inc)
## 🙏 Acknowledgments

- Streamlit team for the amazing framework
- Plotly for interactive visualizations
- Pandas community for data analysis tools