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The simulation of Epsilon-Greedy and Thompson Sampling algorithms for Bayesian A/B Testing. The project shows how both algorithms find the optimal bandit and approximate the rewards of each bandit, given the true reward. Visualizations are done to demonstrate the learning process and convergence.