A powerful, intuitive machine learning platform for the modern data scientist
ru2ya.ai is a comprehensive web-based machine learning platform that revolutionizes the way data scientists work. Built with Django and modern ML frameworks, it provides an intuitive interface for dataset management, model training, and result visualization.
- Upload and manage datasets with ease
- Automated data cleaning and preprocessing
- Advanced data visualization tools
- One-click dataset export
- Support for multiple ML algorithms
- Custom PyTorch Neural Networks
- Automated model training
- Real-time performance tracking
- Interactive data plots
- Real-time training metrics
- Model performance dashboards
- Cluster analysis visualization
- Modern, intuitive interface
- Personalized workspace
- Interactive tutorials
- Model sharing capabilities
| Category | Technologies |
|---|---|
| Backend |  |
| Frontend |  |
| ML & Data |   |
| Visualization |   |
# Clone the repository
git clone https://github.com/username/ru2ya.ai.git
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
npm install
# Run migrations
python manage.py migrate
# Start the server
python manage.py runserverru2ya.ai/
├── DataFlowDesk/ # Main application
│ ├── models.py # Database models
│ ├── views.py # Core logic
│ ├── templates/ # HTML templates
│ ├── static/ # Static files
│ └── templatetags/ # Custom tags
├── datasets/ # Dataset storage
├── media/ # User uploads
└── manage.py # Django management
- Smart Cleaning: Automated detection and handling of data issues
- Advanced Preprocessing: Feature scaling, encoding, and normalization
- Quality Assurance: Comprehensive data quality checks and validation
- Classification: Binary and multi-class classification support
- Regression: Linear, non-linear, and ensemble methods
- Clustering: K-means and hierarchical clustering
- Deep Learning: Customizable neural network architectures
- Data Insights: Distribution analysis and correlation studies
- Model Metrics: ROC curves, confusion matrices, and learning curves
- Interactive Reports: Real-time performance monitoring dashboards
We welcome contributions! Here's how you can help:
- Report bugs and issues
- Propose new features
- Improve documentation
- Submit pull requests
This project is licensed under the MIT License - see the LICENSE file for details.
Made with by the ru2ya.ai team