let me know if any questions. Need help asap.

Consider the training examples shown in Table 1 for a binary classification problem. Customer ID Gender Car TypeClass 1 M Family 00 2 M Sports 3 M Sports 4 M Sports 5 M Sports 00 6 M Sports 7 F Sports 00 8 F Sports 9 F Sports 10 F Luxury 11 M Family 12 M Family 13 M Family 14 M Luxury 15 F Luxury 16 F Luxury 17 F Luxury 18 F Luxury 19 F Luxury 20 F Luxury 555555555 (a)[5 points] Compute the Entropy for the overall collection of training examples. (b)[5 points] Compute the Entropy for the Customer ID attribute. (c)[5 points) Compute the Entropy for the Gender attribute. (d)[5 points) Compute the Entropy for the Car Type attribute using multiway split. 13 Ci 15 16 17 18 19 20 M M F F F F F F Family Luxury Luxury Luxury Luxury Luxury Luxury Luxury Ci (a)(5 points] Compute the Entropy for the overall collection of training examples. (b)[5 points) Compute the Entropy for the Customer ID attribute. (c)[5 points) Compute the Entropy for the Gender attribute. (d)[5 points] Compute the Entropy for the Car Type attribute using multiway split .. (e)[4 points) Which of the three attributes has the lowest Entropy? (n) [5 points) Which of the three attributes will you use for splitting at the root node? Briefly explain your choice