I’m building a small-scale web application and need an experienced developer who can focus first on the data processing and backend logic, then weave in a supervised-learning classification model, and finally wrap everything in a clean, responsive frontend. Here’s what I have in mind: the core of the system is a well-structured backend that ingests raw data, cleans and stores it efficiently, and exposes clear endpoints. Once that foundation is solid, I want a supervised model—likely a standard classifier such as logistic regression, random forest, or something similarly transparent—trained, evaluated, and seamlessly plugged into the API layer. After that, we’ll add a lightweight UI built with plain HTML/CSS/JavaScript or a modern framework if it speeds things up, keeping the design minimal and easy to navigate. Deliverables • Documented backend code (Python preferred, but open) with clean data pipeline • Integrated classification model with evaluation metrics and inference endpoint • Simple, responsive UI demonstrating end-to-end flow • Clear README covering setup, logic, and deployment steps Acceptance criteria • Code is modular, commented, and PEP-8 compliant (or equivalent) • Model achieves agreed-upon baseline accuracy on validation data • Frontend consumes the API smoothly without errors across modern browsers • All tasks delivered on the timeline we set, with progress updates along the way If you’re comfortable juggling solid backend work, supervised ML, and a touch of frontend polish—and you value straightforward communication—let’s talk details.