I’m looking for a Python expert to build a machine-learning pipeline that classifies image data. The task is purely machine learning—no deep learning bells and whistles are necessary unless you believe they will measurably improve accuracy. Here’s what I need: • Clean, modular code for loading the images, handling basic augmentations, splitting data, training, validating, and exporting predictions. • Your choice of mainstream Python libraries—scikit-learn, TensorFlow, PyTorch, or Keras—as long as the environment is easy to reproduce. • A clear README outlining project setup, required packages, and step-by-step instructions so I (or future users) can retrain or fine-tune the model. • Evaluation metrics: accuracy, precision/recall, and a confusion matrix on a held-out validation set. • Inline comments and docstrings that explain the logic and any key design decisions. I’ll supply the labeled images and highlight any class-imbalance issues up front. Ideally, you can suggest sensible preprocessing and augmentation techniques to squeeze out a bit more performance without overcomplicating the solution. If you have prior experience delivering well-documented ML projects for image classification, I’d love to see a short code sample or repo link. Let’s get this working model ready to run, interpret, and iterate on quickly.