Personalized Recommendation in E-Commerce Using a Novel Hybrid Model and Heterogeneous Data

A personalised e-commerce recommendation system built on a novel hybrid model and heterogeneous data.

A personalised e-commerce recommendation system built on a novel hybrid model and heterogeneous data. 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 personalised recommendations ranked by relevance, an order and payment flow, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses Razorpay, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.