6. [3pt] Consider the following data concerning the relationship between academic per formance and salary after graduation. High school GPA and university GPA are two numerical variables (predictors) and salary is the numerical target. Note that salary is measured in thousands of dollars per year. Student ID High School GPA University GPA Salary 1 LOOO'QGO'II-POJNJ 2.2 3.9 3.7 4.0 2.8 3.5 3.8 3.1 3.5 3.4 2.9 3.6 4.0 3.5 1.0 4.0 2.5 3.6 45 55 91 142 88 2600 163 67 unknown Among Students 1 to 8, who is the nearest neighbor to Student 9, using Euclidean distance? Answer the Student ID only. 7. [3pt] In the data set shown above, our task is to predict the salary Student 9 earns after graduation. We apply kNN to this regression problem: the prediction for the numerical target (salary in this example) is equal to the average of salaries for the top 19 nearest neighbors. If k = 3, what is our prediction for Student 9's salary? Round your answer to the nearest integer. Be sure to use the same unit of measure (thousands of dollars per year) as the table above