4. Assume you have the data set shown below. The data contains 10 instances, V1-V11 features, and a binary class (0 and 1). Instance V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 Category
1 1 2 3 1 0 1 3 4 5 10 10 1
2 0 1 2 4 6 3 2 5 5 9 9
3 1 2 4 3 6 5 7 9 8 10 10 0
4 10 3 5 9 8 4 7 6 4 4 0
5 2 1 10 8 9 1000 1 3 7 8 8 0
6 2 2 -100 5 7 8 10 1 1 1
7 9 9 9 9 9 9 9 9 9 9 9 0
8 2 4 6 3 10 1 200 1 7 7 1
9 6 6 6 6 0 0 0 0 0 0 0 1
10 5 5 5 5 5 5 5 5 5 5 5 1

Show all of the problems with the data, apply all of the preprocessing techniques, and perform the classification by KNN algorithm with an 80/20 train-test split ratio. I only need the code for the KNN algorithm.

The can be used to paste text in any order

The volume v and paper surface area a of a conical paper cup are given by where r is the radius of the base of the cone and h is the height of the cone. a. by eliminating h, obtain the expression for a as a function of r and v. b. create a user-de ned function that accepts r as the only argument and computes a for a given value of v. declare v to be global within the function. c. for v ! 10 in.3 , use the function with the fminbnd function to compute the value of r that minimizes the area a. what is the corresponding value of the height h? investigate the sensitivity of the solution by plotting v versus r. how much can r vary about its optimal value before the area increases 10 percent above its minimum value?

1. which of these is not true about torsion bars? a. they can be used to adjust ride height b. they can be adjusted anytime since they don't affect alignment angles c. they attach between the frame and the lower control arm d. they twist to produce a spring effect