I build custom machine learning and deep learning models trained on your data and optimized for real-world accuracy.
What I deliver:
- Image classification models (EfficientNet, ResNet, CNN) — up to 99.98% accuracy
- NLP models for text classification, sentiment analysis, and named entity recognition
- Tabular ML models using XGBoost, Random Forest, and Scikit-learn
- Anomaly detection and fraud detection pipelines
- Full training pipeline with evaluation report, confusion matrix, and ROC curves
- Deployment-ready model files (.h5, .pkl) or REST API
Proven results from my own projects:
- Plant Disease Detection: 99.98% accuracy (87,000+ images)
- Malware Detection: 95%+ accuracy (692 features, XGBoost)
- Phishing URL Detector: 97.09% accuracy (4M URLs, CNN)
You receive clean, well-documented Python code and full support until delivery.