In this post I explore an Austin Housing dataset and predict binned housing price. EDA includes static and interactive geospacial feature maps and feature engineering using natural language processing (NLP). After training/tuning multi-class XGBoost models , I run batch inference to predict the price of Austin, TX houses. I then submit predictions to the Kaggle competition which scrored 0.8876 (mlogloss), which would have placed 6th in the live competition. After submission, I generate SHapley Additive exPlanations (SHAP) plots to understand how XGBoost made predictions.