Hi looking for a step by step breakdown on this question (There is no graph or chart to be provided on this FYI)

An online retailer uses a machine learning algorithm to predict whether an order will be returned or not. For online orders that are eventually returned, the algorithm predicts a return 90% of the time. However, for orders that are not returned, the algorithm erroneously predicts a return 15% of the time. Approximately 20% of orders received by the retailer are returned. (a) For a random order, what is the probability that the algorithm predicts it will be returned? (b) A random order is not returned. What is the probability that it will be predicted as not returned? (c) What is the probability that a random order is not returned and will be predicted as not returned? (d) Suppose an order is predicted to be returned. What is the probability that it is actually returned? (e) For a random order, what is the probability that the algorithm makes a correct prediction (i.e., the order is returned and the algorithm predicts it is returned, or the order is not returned and the algorithm predicts it is not returned)?