Consider the following training corpus of emails with the class labels ham and spam. The content of each email has already been processed and is provided as a bag of words. Email1 (spam): buy car Nigeria profit Email2 (spam): money profit home bank Email3 (spam): Nigeria bank check wire Email4 (ham): money bank home car Email5 (ham): home Nigeria fly Based on this data, estimate the prior probability for a random email to be spam or ham if we don't know anything about its content, i. e. P(Class)? Based on this data, estimate the conditional probability distributions for each word given the class, i. e. P(Word | Class). You can write down these distributions in a table Using the Naive Bayes' approach and your probability estimates, what is the predicted class label for each of the following emails? Show your calculation. o Nigeria Nigeria home o home bank money