ML Kenya - Machine Learning Toolkit
A comprehensive machine learning toolkit specifically designed for African developers and businesses, featuring pre-trained models for common use cases and easy-to-use APIs.
🤖 Features
- Pre-trained Models: Ready-to-use models for image classification, text analysis, and prediction
- Swahili Language Support: Built-in support for Swahili language processing
- African Context: Models trained on African datasets and use cases
- RESTful API: Easy-to-integrate REST API for model serving
- Docker Support: Containerized deployment for easy scaling
- Low Resource: Optimized for low-resource environments
- Offline Capable: Models that can run without internet connectivity
🚀 Quick Start
Installation
``bash
pip install ml-kenya
`
Basic Usage
`python
from ml_kenya import ImageClassifier, TextAnalyzer
Image classification
classifier = ImageClassifier()
result = classifier.predict('path/to/image.jpg')
print(f"Predicted: {result['label']} with confidence {result['confidence']:.2f}")
Text analysis (Swahili support)
analyzer = TextAnalyzer(language='sw')
sentiment = analyzer.sentiment('Hii ni huduma nzuri sana!')
print(f"Sentiment: {sentiment['label']}") # Positive
`
📦 Available Models
Computer Vision
- Crop Disease Detection: Identify diseases in maize, beans, and other crops
- Wildlife Recognition: Identify African wildlife species
- Face Recognition: Facial recognition optimized for African features
- Document Scanner: Extract text from handwritten documents
Natural Language Processing
- Swahili Text Classification: Classify Swahili text into categories
- Sentiment Analysis: Analyze sentiment in multiple African languages
- Named Entity Recognition: Extract entities from African text
- Language Detection: Detect between 50+ African languages
Predictive Analytics
- Market Price Prediction: Predict agricultural commodity prices
- Weather Forecasting: Local weather prediction models
- Customer Churn: Predict customer churn for African businesses
- Fraud Detection: Financial fraud detection optimized for mobile money
🏗️ Architecture
`
ml-kenya/
├── models/ # Pre-trained models
│ ├── vision/ # Computer vision models
│ ├── nlp/ # NLP models
│ └── prediction/ # Predictive models
├── api/ # REST API endpoints
├── utils/ # Utility functions
├── datasets/ # African datasets
└── examples/ # Usage examples
`
🔧 Advanced Usage
Custom Model Training
`python
from ml_kenya import ModelTrainer
trainer = ModelTrainer()
trainer.load_dataset('african_crops')
trainer.train_model('crop_disease_detector')
trainer.save_model('my_custom_model')
`
API Deployment
`python
from ml_kenya import APIServer
server = APIServer()
server.load_model('crop_disease_detector')
server.start(port=8000)
`
Docker Deployment
`bash
Build the image
docker build -t ml-kenya .
Run with pre-loaded models
docker run -p 8000:8000 -e MODELS="crop_disease,text_analyzer" ml-kenya
`
📊 Performance Benchmarks
| Model | Accuracy | Inference Time | Model Size |
| -------------------- | -------- | -------------- | ---------- |
| Crop Disease | 94.2% | 120ms | 45MB |
| Swahili Sentiment | 89.7% | 85ms | 120MB |
| Wildlife Recognition | 91.5% | 200ms | 78MB |
| Price Prediction | 87.3% | 50ms | 25MB |
_Benchmarks run on typical African hardware (4GB RAM, modest CPU)_
🌍 African Context
ML Kenya is built with African developers and businesses in mind:
- Languages: Support for Swahili, Arabic, French, Portuguese, and major African languages
- Use Cases: Agriculture, wildlife conservation, financial services, healthcare
- Infrastructure: Optimized for low-bandwidth and intermittent connectivity
- Data: Trained on African datasets to ensure relevance and accuracy
🔒 Privacy & Security
- Local Processing: Models can run locally without sending data to external servers
- Data Sovereignty: Keep your data within African borders
- Encryption: End-to-end encryption for API communications
- Compliance: GDPR and local data protection regulation compliant
📚 Documentation
Getting Started
Advanced Topics
Examples
🤝 Contributing
We welcome contributions from the African developer community!
Development Setup
`bash
git clone https://github.com/geniusdynamics/ml-kenya.git
cd ml-kenya
pip install -r requirements-dev.txt
pre-commit install
`
Adding New Models
Create model training script in models/
Add model configuration in config/models.yaml
Write tests in tests/`
📄 License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
🙏 Acknowledgments
- African Developers: For their invaluable contributions and feedback
- Research Institutions: For providing datasets and research collaboration
- Open Source Community: For the foundational libraries and tools
- TensorFlow/PyTorch Teams: For the amazing deep learning frameworks
🎯 Impact
ML Kenya has been used in:
- Agriculture: Helping farmers detect crop diseases early
- Conservation: Wildlife monitoring and anti-poaching efforts
- Finance: Fraud detection for mobile money services
- Healthcare: Medical image analysis and diagnosis support
- Education: Language learning and assessment tools
📞 Support
- Documentation: docs.geniusdynamics.com/ml-kenya
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Discord: African ML Community
_Empowering African innovation through accessible machine learning_ 🇰🇪