data analytics

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IBM employs a network of expert analytics consultants for various projects. To help it determine how to distribute its bonuses, IBM wants to form groups of employees with similar performance according to key performance metrics. Each observation (corresponding to an employee) in the flle Big Blue consists of values for UsageRate which corresponds to the proportion of time that the employee has been actively working on high-priority projects. Recognition which is the number of projects for which the employee was specifically requested and Leader which is the number of projects on which the employee has served as project leader Apply k-means clustering with values of k=2 to 7. Normalize the values of the input variables to adjust for the different magnitudes of the variables. How many clusters do you recommend to categorize the employees? Why? 8 A B C D EmployeeID UsageRate Recognition Leader 1 0% 0 0 2 63% 0 0 3 0% 0 0 4 0% 0 0 5 0% 0 0 6 3% 0 0 7 38% 0 94% 0 9 2% 0 11 10 8% 0 0 12 11 26% 0 0 13 12 3% 0 14 13 3% 0 15 14 83% 1 16 15 87% 1 0 17 16 46% 0 0 18 17 18% 0 0 19 18 6% 0 0 20 19 19% 0 0 21 20 19 0 0 22 21 43 0 0 Sheet1 OOO 0 4 86 Sa+ Ready 22 0 0 23 0 0 24 0 | 25 126 0 0 0 0 0 27 0 0 28 2 0 29 0 0 30 0 0 0 3 31 o 32 21 43% 22 16% 23 0% 24 1% 25 16% 26 7% 27 14% 28 45% 29 0% 30 0% 31 12% 32 3% 33 15% 34 28% 35 889 36 58% 1% 1300 0 40 12 50 19 Sheet1 0 0 0 33 3 0 34 0 35 of 36 37 0 0 0 0 0 0 38 OOOOOOOOOO 39 40 0 41 0 42 43 OOO 43 0 0 0 44 0 45 0 0 46 0 0 47 0 42 43 44 45 46 47 48 49 50 51 52 48 0 0 49 0 0 19% 3% 92% 38% 2% 74% 6% 0% 15% 5% 24% 1% 0. 4% 0% 0 50 0 51 0 0 0 52 0 53 0 54 5 5 0 0 0 55 54 0 0 56 0 57 8. 4 2 2 2 2 56 57 58 59 60 18 70 0 0 0 0 0 61 0 0 1 0 0 0 0 59 60 61 62 63 62 63 86% 8596 18% 190 18% 0 64 0 0 Sheet1 65 0 66 64 65 66 0 0 0 67 0 0 68 0 69 3 2 2 0 70 71 0 72 67 68 69 70 71 72 73 74 75 0 0 0 0 73 74 0 25% 6% 3% 65% 91% 63% 7% 41% 12% 27% 53% 5% 29% 61% 76 80% 820 890 23 350 690 480 75 0 0 0 0 76 0 77 OU 0 0 0 0 78 79 - 1 80 81 2 3 OO 76 77 78 79 80 81 82 83 84 85 Om + OOO 82 0 83 84 8 85 crit + 0 0 O 0 0 86 Sheet1 87 3 88 2 0 89 0 0 90 0 0 0 91 92 0 0 93 0 0 0 0 94 0 95 0 96 0 0 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 99% 2% 72% 11% 5% 69% 1% 48% 2% 16% 67% 71% 95% 61% 14 9096 1% 48% 54% 749 239 206 97 0 0 0 98 0 99 100 3 2 0 101 102 103 104 OOOO 0 0 OOP OOR O 0 0 3. 105 106 107 108 2 1 0 Sheet1