Use the columns monthly_spend_k, transactions, avg_balance_k and online_pct, scaled with StandardScaler (call the result X). Fit KMeans(n_clusters=4, n_init=10, random_state=0) on X.
Find the group with the highest average monthly_spend_k. Store the percentage of all customers in that group, rounded to 1 decimal, in result. This is the size of the premium-card audience.
import io
import pandas as pd
cards = pd.read_csv(io.StringIO("""customer_id,monthly_spend_k,transactions,avg_balance_k,online_pct
701,42.9,59,173,69
702,26.6,29,12,54
703,50.7,56,166,78
704,19.1,19,17,41
705,6.7,10,38,8
706,15.3,34,9,53
707,108.3,28,487,29
708,53.8,51,178,74
709,18.9,39,12,45
710,87.9,36,353,44
711,46,65,120,72
712,77.4,32,483,43
713,32.3,52,179,75
714,18.2,33,13,41
715,90.5,28,387,26
716,55.1,55,151,57
717,24.7,37,11,70
718,10.6,18,34,23
719,101.9,16,453,25
720,29.2,49,180,71
721,17.9,33,9,42
722,7.8,11,34,22
723,33.5,64,154,84
724,9.8,15,27,37
725,16.9,25,26,44
726,97.9,20,319,42
727,6.8,13,51,14
728,20.3,27,13,42
729,10.3,16,34,16
730,32.8,53,126,81
731,8.1,11,33,29
732,27.3,23,12,42
733,45.1,52,81,70
734,19.1,35,12,64
735,52,55,148,76
736,54.9,52,135,78
737,7.2,12,53,17
738,10.3,12,53,23
739,15.2,28,19,44
740,47.9,63,128,75
741,84,18,354,25
742,7.5,10,45,26
743,8.7,13,62,38
744,75.6,26,413,53
745,19.5,28,19,48
746,43,44,134,81
747,18,39,14,48
748,12,4,52,15
749,16.6,27,15,56
750,5.4,13,52,26
751,81.9,23,427,29
752,9.1,12,56,15
753,20.8,28,21,45
754,49.3,55,150,73
755,103.5,21,409,24
756,8.9,11,31,15
757,16.9,33,17,38
758,17.5,28,29,26
759,14.1,25,20,35
760,83.1,34,342,47
761,19.7,37,11,31
762,24.1,38,12,41
763,21.2,41,21,39
764,22.6,31,17,45
765,46.5,55,117,67
766,56.2,55,180,84
767,7.7,9,40,30
768,9.1,15,45,5
769,43.7,53,186,67
770,22,37,15,52
771,8.1,10,41,24
772,10,8,38,20
773,17.8,30,9,45
774,6,8,38,9
775,46.8,56,159,68
776,22.5,31,11,43
777,90.3,23,429,37
778,8.4,17,20,33
779,26.9,31,13,39
780,40.6,50,185,71
781,33.2,46,125,84
782,49.9,47,132,73
783,13.5,26,20,49
784,60.9,33,135,86
785,45.3,45,184,79
786,105.3,19,466,33
787,7.7,12,32,36
788,21.1,32,20,32
789,14.2,26,0,20
790,55.4,31,451,23
791,17.2,29,5,39
792,22.3,20,15,56
793,26.8,28,4,40
794,95.5,32,378,51
795,93.6,27,485,20
796,109.6,12,432,45
797,45.3,43,156,77
798,7.8,9,39,20
799,43.2,51,114,87
800,7.3,11,29,15
801,20.8,28,16,37
802,49,47,159,86
803,25.8,31,20,49
804,10.3,14,44,14
805,45.8,52,161,79
806,20.2,32,14,23
807,50.4,48,195,68
808,20.3,22,13,44
809,93.1,29,372,11
810,47.9,52,200,67
811,86.3,23,416,52
812,43.4,54,135,78
813,9.7,10,53,26
814,39.3,58,210,86
815,10.3,14,54,15
816,89.8,26,351,31
817,5.4,20,32,20
818,14.1,34,13,50
819,10.9,12,38,36
820,86.1,19,372,36
821,100.4,26,453,37
822,38.6,57,118,68
823,8.8,15,45,23
824,9,10,37,28
825,18.6,26,16,40
826,39.2,52,127,92
827,67.6,21,462,28
828,7.6,7,54,18
829,81.9,25,440,33
830,23.3,19,21,23
831,21.4,23,13,47
832,20.2,32,20,56
833,86.7,32,509,15
834,7.2,8,48,22
835,12.4,11,30,36
836,16.4,39,19,69
837,54.3,59,152,68
838,39.8,40,174,85
839,10.1,11,37,12
840,4,11,54,23
841,86.4,22,418,32
842,10.2,16,21,19
843,5.9,7,46,36
844,20.5,31,22,38
845,49.1,50,177,76
846,88.4,29,476,10
847,17.3,25,12,53
848,5.9,7,44,16
849,79.6,28,437,16
850,7.3,14,37,18
851,37.5,63,131,81
852,23.4,36,22,52
853,97.4,20,454,38
854,6.8,9,50,7
855,27.7,34,19,51
856,7.2,10,62,26
857,77.3,29,465,22
858,4.2,8,49,15
859,45.1,49,161,69
860,90.2,27,389,13
861,90.3,21,339,21
862,47.7,57,169,77
863,82.4,29,372,36
864,7.6,13,33,14
865,92.5,23,436,30
866,8.8,10,45,14
867,78.6,18,411,35
868,76.3,31,395,35
869,10.7,18,60,28
870,5,13,26,8
871,14.3,36,17,49
872,35.5,57,152,71
873,17.1,28,14,46
874,12.1,13,30,31
875,47.2,50,127,75
876,8.4,13,41,28
877,82,25,381,39
878,18.9,27,20,43
879,44.1,52,180,67
880,15.3,29,12,47
881,47.7,49,145,60
882,81.8,30,290,36
883,12.7,29,2,50
884,22.2,31,19,64
885,8.6,22,41,28
886,17,30,13,47
887,50.4,80,143,63
888,18,31,7,47
889,95.9,25,451,14
890,111.8,19,319,23"""))Store your final answer in a variable named result.
Each hint gives a little more help. Try on your own first.
Run your code to see the output here.