Here is a link to the Google doc:https://docs.google.com/file/d/1XOJDDyQLaC1ucM9OhluMNrZTcCOwzqWz/edit?usp=docslist_api&filetype=msexcel

Question 1 5 Points Consider two people, characterized by the following features. What is the Euclidean distance between them? Height AB 191 24 180 18 SORT((191-180)^2 + (24-18)2) = SQRT(121 + 36) = SORT(157) = 12.53 B ABS(191-180) + ABS(24-18) = 17 SORT((191-24)12 + (180-18) 2) = SQRT(167 2+16242) = 232.6 None of the other answers are correct. Question 2 5 Points According to the rating data in the training spreadsheet, what rating did user 2 give to song 138? A Question 3 5 Points For a user who resides in New York, what three values would you find in the following three columns user_LA, user_NY and user_MI? A 0, 1,0 1 0, 0.1 Question 4 5 Points Exercise Instructions Open the Excel workbook. There are several tabs in thi workbook . The first two tabs contain historic song ratings, randomly partitioned into training. data and test data, respectively. The third tab conta partially populated template for generating k-NN lictions. The remaining tabs contain pairwise distance calculations between each test observation, and each training data observation. For example, the tab named "29-167" contains the pairwise distance calculations between test observation 29-167, and every training data observation. On the k-NN predictions tab, you will find that three sets of predictions have been pre-populated for you. based predictor, le., the average rating that has been provided in the training data for the song ID in question (this is a common, intuitive approach, but it is also unsophisticated), ii) a continuous k-Nearest Neighbor prediction (1.e., kNN regression) and ill) a discrete k-Nearest Neighbor prediction (kNN classification). All three sets or predictions are provided for a set of 20 5 provided for you to capture performance (error) all three approaches, including the continuous popularity and kNN based predictions (MAE, RMSE) and discrete (accuracy, error and a confusion matrix) prediction implementations. As you adjust the value of K, you will see the predictions change, as well as the individual error values for each test observation. Question 5 5 Points Vary the value of k from 1 through 10. Based on the continuous (kNN regression) prediction error measures, what is the optimal number of nearest neighbors employ? A 2 1 4 5 G 7 H : 1 10 Question 6 5 Points basedon the confusion matrix you observe when k = 5 calculate the prediction accuracy of the kNN classifier in cell B30 (Hint: overall accuracy is the proportion of the 20 predictions that were correct, i.e., on the diagonal of the confusion matrix). A 65%% B 359 We cannot answer this question without more Question 7 5 Points Vary the value of k from 1 through 9. Considering the discrete, kNN classifier's predictic accuracy, what is the optimal number of nearest neighbors employ, i.e.. what value of k maximizes the prediction accuracy? A 2 1 4 H 8 Question 8 s Points Yes or no? Would either of these kNN predictive models be useful for generating recommendations related to a new song that has just been released, i.e. which has not yet been rated by any users, or for a new e., a user who has not yet entered any song (Hint: you can assume details on the new user or the new song). A Ye B N Remember to click "Submit" on the lower right of this page to complete your assignment submission