A fully trained machine learning model tailored to the project's objectives, leveraging advanced techniques such as deep learning, XGBoost, or NLP. The package includes well-documented Python code (using libraries like scikit-learn, PyTorch, or Fastai), complete with model performance metrics, feature importance, and clear deployment instructions, if needed. The model is optionally containerized for seamless integration and can be deployed as a REST API. Comprehensive documentation covers data preprocessing steps, training parameters, and best practices for future model improvements. Delivered in Python (Jupyter notebook or script) with up to two revisions​.