[30 marks] (K-Nearest Neighbors) Please show the detailed process of using K- Nearest Neighbor classifier to predict the test instance X= (Speed = 5.20, Weight = 500) is qualified or not, by setting k = 1, 3, and 5, respectively (20 points). Before using KNN classifier, please use Min-max normalization (KNN.pdf page 17) to preprocess the attribute values and plot the preprocessed training data set on a 2d plane (Speed - X axis and Weight - Y axis, the instances of class no are labeled by and the instances of class yes are labeled by + in the plot) (10 points).

2. [30 marks] (K-Nearest Neighbors) Please show the detailed process of using K- Nearest Neighbor classifier to predict the test instance X= (Speed = 5.20, Weight 500) is qualified or not, by setting k=1, 3, and 5, respectively (20 points) Before using KNN classifier, please use Min-max normalization (KNN.pdf page 17) to preprocess the attribute values and plot the preprocessed training data set on a 2d plane (Speed - X axis and Weight - Y axis, the instances of class no are labeled by - and the instances of class yes are labeled by + in the plot) (10 points). ID 1 no 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Speed 2.50 3.75 2.25 3.25 2.75 4.50 3.50 3.00 4.00 4.25 2.00 5.00 8.25 5.75 4.75 5.50 5.25 7.00 7.50 7.25 Weight Qualified 600 no 800 550 no 825 no 750 no 500 no 525 no 325 no 400 no 375 no 200 no 250 no 850 no 875 yes 625 yes 675 yes 950 yes 425 yes 800 yes 575 yes 17 18 19 20