DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling

A hands-on guide to implementing Thompson Sampling in Python to solve the Multi-Armed Bandit problem. Using an email headline optimization scenario, the tutorial walks through building a base simulation class, a random baseline, and a Bayesian bandit subclass using Beta distributions. The simulation compares both approaches across iteration counts from 100 to 1,000,000, showing the bandit approach consistently outperforms random selection by ~20% at scale (10,000+ iterations). Includes a practical checklist for when Thompson Sampling is a good fit: single clear KPI, near-instant feedback, large iteration volume, and distinct arms.