1. Estimating probabilities In this part, you will build a model to try to predict whether or not you will like a movie. The table to the right shows information about 16 movies. The attributes include genre (a-action, d-drama, c-comedy), studio (N Netflix, genre studio rotten like 89 68 81 71 n 80n 741 72 n 58 92 87 n 61 n 86 73 n 71 y 83 y 76 n HBO), rotten (score from Rotten Tomatoes) and like (did you like the movie, y or n). We are interested in calculating the priors and likelihoods for a Naive Bayes (NB) model Follow the directions below to estimate probabilities - - a) Estimate the prior probabilities for both like-n and like-y b) Estimate the likelihoods for genre, studio and rotten given Inke n and nke y. c) Calculate the posterior probabilities: p(like-n | genre=c, studio-N, rotten-95) o p(like-y | genre-c, studio-N, rotten-95) note that vou can use just the numerator, d) Do you think you would like the movie described above (c, N, 95)? 1. Estimating probabilities In this part, you will build a model to try to predict whether or not you will like a movie. The table to the right shows information about 16 movies. The attributes include genre (a-action, d-drama, c-comedy), studio (N Netflix, genre studio rotten like 89 68 81 71 n 80n 741 72 n 58 92 87 n 61 n 86 73 n 71 y 83 y 76 n HBO), rotten (score from Rotten Tomatoes) and like (did you like the movie, y or n). We are interested in calculating the priors and likelihoods for a Naive Bayes (NB) model Follow the directions below to estimate probabilities - - a) Estimate the prior probabilities for both like-n and like-y b) Estimate the likelihoods for genre, studio and rotten given Inke n and nke y. c) Calculate the posterior probabilities: p(like-n | genre=c, studio-N, rotten-95) o p(like-y | genre-c, studio-N, rotten-95) note that vou can use just the numerator, d) Do you think you would like the movie described above (c, N, 95)