Daniel Lara, a human resources manager at a large tech consulting firm, has been reading about using analytics to predict the success of new employees. With the fast-changing nature of the tech industry, some employees have had difficulties staying current in their field and have missed the opportunity to be promoted into a management position. Daniel is particularly interested in whether or not a new employee is likely to be promoted into a management role after 10 years with the company. He gathers information on 300 current employees who have worked for the firm for at least 10 years. The information was based on the job application that the employees provided when they originally applied for a job at the firm. For each employee, the following variables are listed: Promoted (1 if promoted within 10 years, 0 otherwise), GPA (college GPA at graduation), Sports (number of athletic activities during college), and Leadership (number of leadership roles in student organizations). A portion of the HR_Data worksheet is shown in the accompanying table.

a. Use the HR_Data worksheet to help Daniel perform KNN analysis to determine whether or not an employee is likely to be promoted into a management role after 10 years with the company. Score the records of the 10 new employees in the HR_Score worksheet. What is the optimal k? What is the predicted outcome for the first new employee? 

b. What is the misclassification rate for the optimal k? 

c. Report the accuracy, specificity, sensitivity, and precision rates for the test data set (for Analytic Solver) or validation data set (for R). 

d. Display the cumulative lift chart, decile-wise chart, and ROC curve. 

e. Comment on the performance of the KNN classification model. Is the KNN classification an effective way to predict an employee’s success?  

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Promoted 0 1 I 0 GPA 3.28 3.93 ⠀ 3.54 Sports 0 6 B 5 Leadership 2 3 ⠀ 0