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.
Store the inertia (sum of squared distances to the nearest centre), rounded to 1 decimal, in result.
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.