You will apply k-means clustering method to Mall Customer Data. k will ve determined by you. Please apply the necessary data preprocessing steps before applying the algorithms. You can use either weka or excel to do it. If you prefer excel please perform at least 2 iterations in algorithms.

CustomerID Gender Age Annual Income (k$) Spending Score (1-100) 1 Male 19 15 39 2 Male 21 15 81 3 Female 20 16 6 4 Female 23 16 77 5 Female 31 17 40 6 Female 22 17 76 7 Female 35 18 6 8 Female 23 18 94 9 Male 64 19 3 10 Female 30 19 72 11 Male 67 19 14 12 Female 35 19 99 13 Female 58 20 15 14 Female 24 20 77 15 Male 37 20 13 16 Male 22 20 79 17 Female 35 21 35 18 Male 20 21 66 19 Male 52 23 29 20 Female 35 23 98 21 Male 35 24 35 22 Male 25 24 73 23 Female 46 25 5 24 Male 31 25 73 25 Female 54 28 14 26 Male 29 28 82 27 Female 45 28 32 28 Male 35 28 61 29 Female 40 29 31 30 Female 23 29 87 31 Male 60 30 4 32 Female 21 30 73 33 Male 53 33 4 34 Male 18 33 92 35 Female 49 33 14 36 Female 21 33 81 37 Female 42 34 17 38 Female 30 34 73 39 Female 36 37 26 40 Female 20 37 75 41 Female 65 38 35 42 Male 24 38 92 43 Male 48 39 36 44 Female 31 39 61 45 Female 49 39 28 46 Female 24 39 65 47 Female 50 40 55 48 Female 27 40 47 49 Female 29 40 42 50 Female 31 40 42 51 Female 49 42 52 52 Male 33 42 60 53 Female 31 43 54 54 Male 59 43 60 55 Female 50 43 45 56 Male 47 43 41 57 Female 51 44 50 58 Male 69 44 46 59 Female 27 46 51 60 Male 53 46 46 61 Male 70 46 56 62 Male 19 46 55 63 Female 67 47 52 64 Female 54 47 59 65 Male 63 48 51 66 Male 18 48 59 67 Female 43 48 50 68 Female 68 48 48 69 Male 19 48 59 70 Female 32 48 47 71 Male 70 49 55 72 Female 47 49 42 73 Female 60 50 49 74 Female 60 50 56 75 Male 59 54 47 76 Male 26 54 54 77 Female 45 54 53 78 Male 40 54 48 79 Female 23 54 52 80 Female 49 54 42 81 Male 57 54 51 82 Male 38 54 55 83 Male 67 54 41 84 Female 46 54 44 85 Female 21 54 57 86 Male 48 54 46 87 Female 55 57 58 88 Female 22 57 55 89 Female 34 58 60 90 Female 50 58 46 91 Female 68 59 55 92 Male 18 59 41 93 Male 48 60 49 94 Female 40 60 40 95 Female 32 60 42 96 Male 24 60 52 97 Female 47 60 47 98 Female 27 60 50 99 Male 48 61 42 100 Male 20 61 49 101 Female 23 62 41 102 Female 49 62 48 103 Male 67 62 59 104 Male 26 62 55 105 Male 49 62 56 106 Female 21 62 42 107 Female 66 63 50 108 Male 54 63 46 109 Male 68 63 43 110 Male 66 63 48 111 Male 65 63 52 112 Female 19 63 54 113 Female 38 64 42 114 Male 19 64 46 115 Female 18 65 48 116 Female 19 65 50 117 Female 63 65 43 118 Female 49 65 59 119 Female 51 67 43 120 Female 50 67 57 121 Male 27 67 56 122 Female 38 67 40 123 Female 40 69 58 124 Male 39 69 91 125 Female 23 70 29 126 Female 31 70 77 127 Male 43 71 35 128 Male 40 71 95 129 Male 59 71 11 130 Male 38 71 75 131 Male 47 71 9 132 Male 39 71 75 133 Female 25 72 34 134 Female 31 72 71 135 Male 20 73 5 136 Female 29 73 88 137 Female 44 73 7 138 Male 32 73 73 139 Male 19 74 10 140 Female 35 74 72 141 Female 57 75 5 142 Male 32 75 93 143 Female 28 76 40 144 Female 32 76 87 145 Male 25 77 12 146 Male 28 77 97 147 Male 48 77 36 148 Female 32 77 74 149 Female 34 78 22 150 Male 34 78 90