Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

A/B checking is a regular measure in many marketing procedures for e-Commerce companies. Through well-designed research, advertisers can gain insight about when and how efforts be maximized active promotions driven. Whilst algorithms the problem are theoretically well developed, empirical confirmation typically restricted. In practical terms, standard experimentation makes less money relative to more advanced machine learning methods. This paper presents thorough study of most popular multi-strategy algorithms. Three important observations made from our results. First, simple heuristics such as Epsilon Greedy Thompson Sampling outperform sound settings by significant margin. this report, state testing addressed, some typical (Multi-Arms Bandits) used optimize described comparable. We found that Greedy, an exceptional winner payouts situation.