Implement a Naive Bayes classifier (must write own code) to predict whether it is a good day to play tennis based on the information in the training data. Assume a for Laplacian is 0.8. Also, you don't need to take the log. Use the last 10 rows in this file for the test and all other rows for training. 1. (2 points) What's the prior distribution for playing/not playing tennis? 2. (2 points) Determine the likelihood for outlook (sunny/not sunny). 3. (2 points) Determine the likelihood for temperature (hot/not hot). 4. (2 points) Determine the likelihood for wind (windy/not windy). 5. (5 points) What are the posterior probabilities for the test patterns? 6. (2 points) What is the classification accuracy rate on the test set? data: Sunny Hot Windy Play Yes Yes Yes Yes Yes Yes Yes Yes 2 Yes Yes Yes No 3 Yes Yes Yes NO 4 Yes No Yes NO 5 Yes No Yes Yes 6 Yes No Yes Yes 7 Yes No Yes NO 8 Yes NO No NO 9 No NO No Yes 10 No NO No NO 11 No NO No NO 12 No NO No Yes 13 No NO No Yes 14 Yes No Yes NO 15 Yes Yes Yes Yes 16 Yes Yes Yes Yes 17 Yes Yes Yes No 18 Yes Yes Yes No Yes No 19 Yes NO 20 Yes No Yes Yes 8 1