Explainable Hybrid AI Framework for E-Commerce Decision Intelligence with an Evidence-Grounded Intelligent Assistant

An explainable hybrid AI framework for e-commerce decision intelligence with an evidence-grounded intelligent assistant.

An explainable hybrid AI framework for e-commerce decision intelligence with an evidence-grounded intelligent assistant. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), a conversational interface, an order and payment flow, data preparation and exploration, and model training and comparison of algorithms. The reference build uses SHAP, Razorpay, Python, scikit-learn, and Pandas, with a clear project structure, setup instructions and sample data so that it can be run and extended.