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How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example #
Introduction #
We live in a golden age of data-driven decision-making. Not only do most organizations maintain massive databases of information, but they also have countless teams that rely on this data to inform their decision-making. From clickstream traffic to wearable edge devices, telemetry, and much more, the speed and scale of data-driven decision-making are increasing exponentially, driving the popularity of integrating machine learning and AI frameworks.
Speaking of data-driven decision-making frameworks, one of the most reliable and time-tested approaches is A/B testing. A/B testing is especially popular among websites, digital products, and similar outlets where customer feedback in the form of clicks, orders, etc., is received nearly instantly and at scale. What makes A/B testing such a powerful decision framework is its ability to control for countless variables, allowing a stakeholder to see the effect of the element they are introducing in the test on a key performance indicator (KPI).