In this problem, using Python the healthcareTrain.csv and healthcareTest.csv . Youll use the Value Distance Metric (VDM) to find the distance

between symbolic feature values Northeast, Midwest, South, and West, and further use this information in KNN algorithm to predict pdc-80-flag. region: US Census Region (1 Northeast, 2 Midwest, 3 South, 4 West)

1. (10 points) Find all the relevant conditional probabilities for finding VDM for symbolic variable region and report your results in a table.

2. (10 points) Use results in part 1 to find the distance between symbolic feature values Northeast, Midwest, South, and West using VDM equation.

Report the distances in a table.

3. (10 points) Use this variable (region) in conjunction with the variables of problem 1 and regenerate your model, for k = 75 to 105 with a step

size of 2. Report the mean accuracy rate. Compare this mean with mean accuracy rate from previous problem. Has it increased for decreased?

RegionTrain: 0 2 1 2 2 2 3 3 4 4 5 3 6 3 7 3 8 3 9 4 10 3 11 3 12 2 13 2 14 3 15 3 16 4 17 1 18 3 19 1 

RegionTest:

0 3 1 3 2 3 3 3 4 3 5 3 6 3 7 3 8 3 9 3 10 3 11 4 12 4 13 4 14 4 15 4 16 4 17 4 18 3 19 4 

Trainpdc80:

0 0 1 1 2 0 3 0 4 1 5 1 6 1 7 1 8 0 9 0 10 0 11 1 12 0 13 1 14 1 15 1 16 1 17 1 18 0 19 0 

Testpdc80:

0 0 1 1 2 0 3 0 4 1 5 1 6 1 7 1 8 0 9 0 10 0 11 1 12 0 13 1 14 1 15 1 16 1 17 1 18 0 19 0