Your tasks in this problem are as follows: 1. Load the data set into WEKA and under the Classify tab choose classifiers.bayes.NaiveBayesSimple. Under the Test options select Use training set. Then run the classifier and save the result set buffer. You will notice that the model specified the conditional probabilities associated with different attributes for each of the two classes (Visit_Again=yes and Visit_Again=no). For example, using this information you can find Pr(Browsed=no | Visit_Again=yes) or Pr(searched=yes | Visit_Again=no). Also, the model includes the prior probabilities of each of the two classes, Pr(Visit_Again=no) and Pr(Visit_Again=yes). Submit your result set as part of your answer. 2. Next, using the probabilities you obtained from the model and Bayes' Rule, manually compute the probabilities of each of the following two new instances belonging each of the two classes: a. New instance X = b. New instance Y = For example, in the case of X, you must user Bayes' rule to compute Pr(X | Visit_Again=yes) and Pr(X | Visit_Again=no), and similarly for Y. Show the details of your computation