1. Build a decision tree by taking as input a maximum depth and by randomly splitting the dataset as 80/20 split i.e., 80% for training and 20% for testing. Provide the accuracy by averaging over 10 random 80/20 splits. Consider that particular tree which provides the best test accuracy as the desired one.
2. What is the best possible depth limit to be used for your dataset. Provide a plot
explaining the same.
3. Perform the pruning operation over the tree obtained in question 2 using a valid
statistical test for comparison.
4. Print the final decision tree obtained from question 3 following the hierarchical levels of
data attributes as nodes of the tree.
A brief report explaining the procedure and the results
