Custom Classification ML Application

I’m looking to develop an end-to-end machine learning application focused on classification. The core objective is straightforward: feed the model with curated data, train it to distinguish between the defined classes, and expose the resulting predictions through a clean interface or API that I can plug into my existing workflow. You’ll be free to select the most appropriate algorithms—whether that ends up being a tried-and-true random forest, gradient boosting, or deep-learning architecture—as long as the final system is accurate, explainable where possible, and easy for me to retrain when fresh data comes in. I value clear, commented code (Python preferred), a concise README, and a demonstration notebook or script that shows how to prepare the data, fit the model, evaluate performance, and make inferences. Acceptance criteria • Minimum F1-score or accuracy target we agree on during kickoff • Reproducible training pipeline (virtual-env or Docker) • Inference endpoint or CLI producing class labels on new records If you’ve built similar classifiers and can move quickly from data ingestion through model deployment, let’s discuss the details and timelines.

Project ID: 40420854