I’m building out an email-marketing tool that relies on machine-learning to drive its core features. The heart of the project is, quite simply, “email marketing tools” built in Python, so every line of code you write should keep that use-case front and center. Here’s what I need from you: • A clean, well-documented Python pipeline that ingests raw email-campaign data, pre-processes it, then trains and evaluates a model. • The finished model packaged so it can be dropped into my current codebase with minimal refactoring—think scikit-learn, pandas, NumPy and any lightweight helper libraries you feel are essential. • A short README explaining how to reproduce your results and extend the model for future campaigns. Acceptance criteria 1. Running one command should download or read the sample dataset, train the model, and output metrics. 2. The codebase passes a quick lint/flake8 check and is pep8-compliant. 3. Results are reproducible on Python 3.10+ with only the dependencies you list. If you’ve previously worked on predictive features for email platforms—such as subject-line optimization, send-time recommendations, or engagement scoring—you’ll feel right at home. Let me know the most relevant project you’ve tackled, and how quickly you can turn this around.