My project centers on building an AI–driven solution in Python. I am still finalising whether the end product will lean toward a traditional machine-learning model, an NLP workflow, or a computer-vision pipeline, so I welcome suggestions on the most suitable approach once you understand the use-case. The core activities will involve shaping the problem statement with me, designing the model architecture, writing clean, modular Python code, and setting up training and evaluation routines. I may supply a dataset if it fits, or we might have to gather and prepare the data together; either way, data-handling best practices and thorough documentation are essential. Deliverables • A clear, version-controlled codebase (Git) in Python • Reproducible training / testing scripts or notebooks • A concise README explaining setup, usage, and model limitations • Brief hand-over call or walkthrough video at project close If this scope aligns with your skill set, tell me how you would approach the design and what libraries or frameworks—TensorFlow, PyTorch, scikit-learn, spaCy, OpenCV, or others—you feel would be most effective.