A hands-on data science project comparing Linear Regression and XGBoost for retail sales forecasting. This project reveals an unexpected outcome – the simpler model wins – and explores why model selection matters more than algorithm complexity in real-world applications.

A guided walkthrough of modeling count data with excess zeros. This project compares Poisson, Negative Binomial, Zero‑Inflated (ZIP/ZINB), Hurdle, and Bayesian (JAGS) models on simulated cargo shipment data and the real bioChemists dataset. Model selection is driven by AIC and DIC, demonstrating when structural zero processes are justified – and when simpler models win. Includes practical notes on NaN AIC/DIC issues and numerical stability.