Explainable Machine Learning for Reliable House Price Prediction

An explainable machine learning system for reliable house price prediction.

An explainable machine learning system for reliable house price prediction. 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), data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses SHAP, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.