Please help with this problem

3. Decision Trees and Information Gain. Below is a table (also provided as dosage.csv") from a hypothetical medical study. The data have three attributes, dosageA, dosageB, dosageC, and a result. Result has three values, low, medium and high. dosageA dosageB dosage result 4 2 3 high 1 10 1 low 3 3 3 medium 8 1 8 high 1 1 2 low 5 1 7 medium 9 4 4 medium 1 5 4 medium 7 7 6 medium 1 2. 10 high 5 2 2 high 6 4 6 medium 3 9 9 low 6 6 3 medium 9 4 5 9 9 9 5 high 9 low 9 medium 1 low 9 low 1 1 4 7 6 1 5 9 3 5 1 low 2 medium 1 medium 8 medium 1 medium 8 high 1 8 8 1 6 1 a. Build a decision tree. Show your decision tree (by drawing it out or by including the Weka output). Ensure all nodes and branches are legible. b. Calculate the information gain using Gini or Entropy at each decision node in your tree. Show your work. How would your decision tree classify the data below? C. dosagea dosageB dosagec result 2 2 8 ? | 219 10 3 ? 7 1 3 ? 6 9 2 ? 5 ? 8 9 2 7 1 ? 4 6 6 ? 9 1 4 ? d. What are the rules for pruning the tree you built? Should you prune the tree you built above? Why or why not