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Write a function to find the optimal k (the k value which minimizes the classification error) and call it optimal_k.

# Exercise #1: List the Nine_neighbors # your code here Nine_balance<-arrange(names, income)[
Exercise 2 Use knn function in class package and predict labels in the test data with knn when k=5. Use set.seed(4230) and name the p # Exercise #3: knn results when k=10 # your code here #
Exercise #4: Performance measure # your code here 0
Exercise 5: Write a function to find the optimal k (the k value which minimizes the classification error) and call it optimal_k. In other words, at which value of k does the k_class_error take the minimum value?

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