Datasets:
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limit_bal
float64 10k
1M
| age
float64 21
79
| pay_0
float64 -2
8
| pay_2
float64 -2
8
| pay_3
float64 -2
8
| pay_4
float64 -2
8
| pay_5
float64 -2
8
| pay_6
float64 -2
8
| bill_amt1
float64 -165,580
965k
| bill_amt2
float64 -69,777
984k
| bill_amt3
float64 -157,264
1.66M
| bill_amt4
float64 -170,000
892k
| bill_amt5
float64 -81,334
927k
| bill_amt6
float64 -209,051
962k
| pay_amt1
float64 0
874k
| pay_amt2
float64 0
1.68M
| pay_amt3
float64 0
896k
| pay_amt4
float64 0
621k
| pay_amt5
float64 0
427k
| pay_amt6
float64 0
527k
| sex:1
float64 0
1
| sex:2
float64 0
1
| education:0
float64 0
1
| education:1
float64 0
1
| education:2
float64 0
1
| education:3
float64 0
1
| education:4
float64 0
1
| education:5
float64 0
1
| education:6
float64 0
1
| marriage:0
float64 0
1
| marriage:1
float64 0
1
| marriage:2
float64 0
1
| marriage:3
float64 0
1
| default.payment.next.month
int64 0
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
80,000
| 24
| 0
| 0
| 0
| 0
| 0
| 0
| 75,125
| 77,353
| 78,321
| 73,731
| 39,643
| 39,457
| 3,503
| 5,001
| 2,092
| 1,218
| 1,445
| 878
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
30,000
| 28
| 0
| 0
| 0
| 0
| 0
| 0
| 29,242
| 29,507
| 29,155
| 25,255
| 22,001
| 0
| 5,006
| 1,244
| 851
| 955
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
180,000
| 44
| 0
| 0
| -1
| -1
| -1
| -1
| 20,916
| 0
| 850
| 0
| 6,881
| 10,340
| 0
| 850
| 0
| 6,881
| 10,340
| 182
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
|
60,000
| 25
| 0
| 0
| 0
| 0
| 0
| 0
| 58,839
| 53,235
| 38,533
| 39,639
| 39,619
| 39,140
| 2,018
| 1,900
| 2,000
| 1,500
| 1,900
| 2,000
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
130,000
| 25
| 0
| 0
| 0
| 0
| 0
| 0
| 111,587
| 112,348
| 114,734
| 117,823
| 120,854
| 123,904
| 4,100
| 4,200
| 5,000
| 5,000
| 5,000
| 10,700
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
20,000
| 32
| 1
| 2
| 0
| 0
| 0
| 0
| 19,844
| 19,238
| 20,205
| 19,588
| 20,037
| 19,880
| 0
| 1,302
| 685
| 748
| 697
| 690
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
100,000
| 33
| -1
| -1
| -1
| -1
| -1
| 0
| 7,067
| -418
| 7,064
| 15,229
| 9,689
| 2,669
| 0
| 7,482
| 15,315
| 9,705
| 0
| 4,600
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
210,000
| 31
| 0
| 0
| 0
| 0
| 0
| 0
| 205,243
| 209,502
| 203,831
| 178,410
| 130,619
| 115,700
| 7,736
| 7,100
| 8,300
| 4,800
| 4,396
| 4,200
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
50,000
| 35
| 0
| 0
| 0
| 0
| 0
| 0
| 13,517
| 14,536
| 15,694
| 16,431
| 17,056
| 17,581
| 1,550
| 1,700
| 1,300
| 900
| 800
| 800
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
360,000
| 43
| -2
| -2
| -2
| -2
| -2
| -2
| 4,435
| 799
| 1,071
| 15,584
| 3,195
| 4,261
| 805
| 1,071
| 15,604
| 3,195
| 4,269
| 3,525
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
220,000
| 38
| -1
| -1
| -1
| -1
| -1
| -1
| 22,145
| 5,529
| 4,688
| 1,621
| 8,522
| 4,149
| 5,575
| 4,716
| 1,632
| 8,559
| 4,164
| 10,626
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
70,000
| 37
| 0
| 0
| 2
| 0
| 0
| 0
| 67,374
| 70,890
| 66,782
| 67,266
| 66,431
| 67,046
| 6,044
| 0
| 2,975
| 2,505
| 2,590
| 2,610
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
100,000
| 27
| -1
| 2
| 0
| 0
| 0
| 0
| 17,553
| 10,628
| 5,836
| 6,746
| 7,889
| 0
| 0
| 1,000
| 2,000
| 3,323
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
80,000
| 30
| 2
| 0
| 0
| -1
| -1
| -2
| 4,794
| 4,989
| 2,065
| 1,000
| 0
| 0
| 1,074
| 1,000
| 1,000
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
120,000
| 61
| 1
| 2
| 0
| 0
| 0
| 0
| 121,709
| 78,369
| 61,849
| 57,737
| 59,174
| 60,651
| 99
| 8,800
| 2,700
| 3,000
| 2,600
| 2,200
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
260,000
| 45
| 0
| 0
| 0
| 0
| 0
| 0
| 21,480
| 25,791
| 31,654
| 35,784
| 40,726
| 45,923
| 5,000
| 6,781
| 5,000
| 6,452
| 6,696
| 6,892
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
150,000
| 24
| 0
| 0
| 0
| 0
| 0
| 0
| 143,212
| 137,963
| 139,520
| 99,814
| 100,810
| 103,233
| 5,500
| 5,500
| 4,500
| 3,500
| 4,000
| 4,000
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
270,000
| 39
| 0
| 0
| 0
| 0
| 0
| 0
| 116,168
| 118,661
| 121,106
| 118,269
| 120,737
| 123,227
| 4,173
| 4,275
| 4,054
| 4,191
| 4,302
| 4,567
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
170,000
| 31
| 0
| 0
| 0
| 0
| 0
| 0
| 3,062
| 112,762
| 112,880
| 111,210
| 111,525
| 112,360
| 112,000
| 6,000
| 4,500
| 4,500
| 5,000
| 4,500
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
140,000
| 24
| -1
| -1
| -1
| -1
| -1
| 0
| 16,343
| 1,462
| 4,326
| 6,380
| 48,866
| 23,797
| 1,462
| 4,326
| 6,398
| 48,866
| 3,500
| 2,000
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
110,000
| 29
| 0
| 0
| 0
| 0
| 0
| 0
| 101,006
| 104,474
| 106,332
| 106,906
| 108,952
| 110,444
| 5,300
| 5,300
| 4,000
| 4,100
| 4,631
| 4,404
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
430,000
| 48
| 0
| 0
| 0
| 0
| 0
| 0
| 32,263
| 32,802
| 25,645
| 25,045
| 25,568
| 26,144
| 1,794
| 2,399
| 884
| 914
| 987
| 869
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
360,000
| 39
| 0
| 0
| -2
| -2
| -2
| -2
| 12,768
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
150,000
| 45
| 0
| 0
| 0
| 0
| 0
| 0
| 108,434
| 111,178
| 32,220
| 33,247
| 34,164
| 34,878
| 3,000
| 1,300
| 1,300
| 1,200
| 1,200
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
240,000
| 46
| 2
| 2
| -2
| -1
| 0
| -1
| 456
| 0
| 0
| 2,240
| 1,681
| 2,267
| 0
| 0
| 2,240
| 0
| 2,267
| 3,074
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
200,000
| 33
| -1
| -1
| -1
| -2
| -2
| -2
| 846
| 4,292
| 0
| 0
| 0
| 0
| 4,974
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
50,000
| 27
| 0
| 0
| 0
| 0
| 0
| 2
| 44,421
| 45,897
| 46,920
| 47,864
| 50,856
| 48,390
| 2,500
| 2,100
| 2,000
| 3,900
| 0
| 2,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
390,000
| 38
| 0
| 0
| 0
| 0
| 0
| 0
| 164,418
| 167,501
| 134,282
| 128,701
| 131,529
| 135,242
| 9,000
| 7,027
| 5,000
| 5,000
| 6,000
| 5,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
240,000
| 37
| -1
| 2
| -1
| -1
| -1
| 0
| 1,769
| 842
| 14,015
| 0
| 1,317
| 566
| 0
| 14,015
| 0
| 1,317
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
220,000
| 33
| 1
| -1
| -1
| 0
| 0
| 0
| -117
| 487
| 2,879
| 4,483
| 6,087
| -273
| 1,000
| 3,000
| 2,000
| 2,000
| 0
| 2,000
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
230,000
| 59
| -1
| 0
| 0
| 0
| 0
| 0
| 208,459
| 206,331
| 203,813
| 201,331
| 198,999
| 191,671
| 7,536
| 7,277
| 7,100
| 7,120
| 6,844
| 6,945
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
320,000
| 29
| -2
| -2
| -2
| -2
| -2
| -2
| 9,161
| 26,156
| 13,185
| 17,439
| 66,408
| 14,333
| 28,524
| 13,270
| 17,562
| 66,751
| 14,405
| 52,677
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
100,000
| 47
| 0
| 0
| 0
| 0
| 0
| 0
| 43,175
| 42,669
| 42,238
| 41,646
| 40,976
| 40,503
| 1,680
| 1,722
| 1,603
| 1,406
| 1,598
| 1,502
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
480,000
| 46
| -1
| -1
| -1
| -2
| -1
| 0
| 993
| 1,317
| 0
| 0
| 4,415
| 1,978
| 1,317
| 0
| 0
| 4,415
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
20,000
| 24
| 0
| 0
| -1
| 2
| 0
| 0
| 10,476
| 7,279
| 688
| 688
| 688
| 1,320
| 1,000
| 688
| 0
| 0
| 650
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
420,000
| 32
| 0
| 0
| 0
| 0
| 0
| 0
| 546,485
| 228,070
| 184,810
| 131,304
| 110,930
| 84,193
| 18,546
| 8,931
| 4,940
| 1,796
| 3,100
| 133,131
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
160,000
| 55
| 0
| 0
| 0
| 0
| 0
| 0
| 155,389
| 152,162
| 154,715
| 155,026
| 79,051
| 81,089
| 6,911
| 6,500
| 4,270
| 2,518
| 2,994
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
380,000
| 40
| 0
| 0
| 0
| 0
| 0
| 0
| 231,759
| 233,357
| 236,292
| 237,176
| 236,379
| 239,221
| 10,000
| 9,000
| 10,021
| 10,000
| 10,000
| 10,000
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
50,000
| 29
| 0
| 0
| 2
| 2
| 2
| 0
| 24,043
| 26,806
| 26,077
| 27,898
| 27,315
| 27,709
| 3,165
| 0
| 2,257
| 0
| 1,000
| 2,000
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
230,000
| 26
| -2
| -2
| -2
| -2
| -2
| -2
| 416
| 371
| 416
| 416
| 566
| 416
| 371
| 461
| 416
| 566
| 416
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
50,000
| 35
| 0
| 0
| 0
| 0
| 0
| 0
| 47,816
| 49,694
| 48,543
| 46,366
| 9,076
| 9,812
| 3,000
| 3,000
| 3,019
| 3,000
| 1,000
| 2,000
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
80,000
| 38
| 0
| 0
| 0
| 0
| 0
| 0
| 19,277
| 20,060
| 20,977
| 20,695
| 18,587
| 20,767
| 1,400
| 1,306
| 1,019
| 685
| 2,500
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
80,000
| 26
| 0
| 0
| 0
| 0
| 0
| 0
| 67,229
| 24,772
| 20,000
| 20,000
| 20,000
| 0
| 2,089
| 6,000
| 200
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
30,000
| 38
| 0
| 0
| 2
| 0
| 0
| 0
| 27,429
| 29,085
| 29,282
| 29,270
| 29,078
| 28,652
| 2,403
| 1,200
| 588
| 588
| 574
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
280,000
| 30
| -1
| -1
| -1
| -1
| -1
| -1
| 17,913
| 380
| 5,118
| 380
| 380
| 380
| 380
| 5,118
| 380
| 380
| 380
| 380
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
100,000
| 47
| 1
| 2
| 0
| 0
| 0
| 0
| 99,823
| 96,932
| 96,924
| 96,122
| 97,432
| 95,062
| 0
| 3,579
| 3,472
| 3,287
| 3,590
| 10,179
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
130,000
| 46
| 0
| 0
| 0
| -2
| -2
| -2
| 125,557
| 124,900
| 0
| 0
| 0
| 0
| 2,498
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
120,000
| 50
| 0
| 0
| 0
| 0
| 0
| 0
| 118,492
| 119,637
| 102,228
| 75,558
| 47,536
| 44,749
| 6,141
| 4,474
| 2,647
| 1,367
| 1,322
| 1,500
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
30,000
| 23
| 0
| 0
| 2
| 2
| 2
| -1
| 19,321
| 23,985
| 23,321
| 25,809
| 24,985
| 700
| 5,320
| 0
| 3,316
| 0
| 700
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
50,000
| 23
| 2
| 2
| 2
| 2
| 2
| 2
| 42,304
| 45,327
| 46,294
| 47,147
| 46,272
| 49,050
| 3,695
| 2,000
| 1,900
| 0
| 3,700
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
220,000
| 33
| -1
| -1
| -1
| -1
| -1
| -1
| 3,378
| 1,531
| 942
| 608
| 1,738
| 277
| 1,531
| 942
| 608
| 1,738
| 277
| 492
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
20,000
| 27
| 0
| 0
| 0
| 0
| -1
| 2
| 20,443
| 19,038
| 38,730
| -210
| 690
| 150
| 1,558
| 1,400
| 400
| 1,290
| 0
| 780
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
50,000
| 47
| 0
| 0
| 0
| 0
| 0
| 0
| 11,961
| 14,259
| 16,028
| 17,760
| 19,462
| 22,141
| 2,500
| 2,000
| 2,000
| 2,000
| 3,000
| 3,000
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
80,000
| 23
| 0
| 0
| 0
| -2
| -2
| -2
| 8,766
| 4,777
| 0
| 0
| 0
| 0
| 1,600
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
110,000
| 24
| 1
| 2
| 0
| 0
| 0
| 0
| 115,527
| 109,871
| 110,565
| 216,850
| 110,952
| 109,169
| 0
| 4,000
| 7,000
| 7,766
| 3,982
| 5,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
360,000
| 38
| -1
| -1
| -1
| -2
| -2
| -2
| 176
| 252
| 0
| 0
| 0
| 0
| 252
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
70,000
| 26
| 2
| 0
| 0
| 0
| 0
| 0
| 66,087
| 67,510
| 69,007
| 61,845
| 60,184
| 66,801
| 3,120
| 3,303
| 2,800
| 2,200
| 7,600
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
30,000
| 22
| 0
| -1
| -1
| -1
| -1
| -1
| 3,118
| 2,411
| 1,260
| 1,179
| -101
| 890
| 2,414
| 1,260
| 1,181
| 101
| 991
| 579
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
460,000
| 28
| -1
| -1
| 0
| -1
| -1
| -1
| 1,358
| 55,022
| 50,394
| 3,917
| 1,695
| 22,231
| 55,071
| 1,263
| 3,934
| 1,702
| 22,238
| 513
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
220,000
| 65
| -1
| -1
| -1
| -1
| -1
| -1
| 1,193
| 1,525
| 3,067
| 1,771
| 2,326
| 390
| 1,525
| 3,470
| 1,771
| 2,333
| 390
| 2,361
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
50,000
| 55
| 0
| 0
| 0
| 0
| 0
| 0
| 45,555
| 41,372
| 41,544
| 13,007
| 16,233
| 14,952
| 1,968
| 1,843
| 434
| 5,000
| 582
| 679
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
|
160,000
| 54
| -1
| 2
| -1
| 0
| 0
| 0
| 780
| 390
| 1,560
| 1,170
| 780
| 390
| 0
| 1,560
| 0
| 0
| 0
| 390
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
210,000
| 30
| 0
| 0
| 2
| 0
| 0
| 0
| 99,342
| 99,506
| 100,117
| 101,212
| 101,197
| 103,175
| 4,900
| 4,100
| 3,600
| 4,500
| 4,000
| 3,900
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
290,000
| 38
| -2
| -2
| -2
| -1
| 0
| 0
| 0
| 130
| 0
| 24,756
| 25,147
| 25,685
| 130
| 0
| 24,756
| 899
| 942
| 927
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
200,000
| 29
| -1
| -1
| -1
| -2
| -2
| -2
| 8,951
| 6,595
| 0
| 0
| 0
| 0
| 9,117
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
460,000
| 28
| 0
| 0
| -1
| -1
| -1
| 0
| 17,919
| 13,041
| 9,681
| 3,284
| 732
| 2,732
| 1,500
| 9,681
| 3,284
| 732
| 2,000
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
350,000
| 55
| -1
| -1
| -1
| -1
| -1
| -1
| 3,297
| 11,634
| 12,952
| 39,274
| 5,474
| 14,837
| 11,637
| 12,991
| 39,278
| 6,000
| 14,837
| 931
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
70,000
| 42
| 0
| 0
| 0
| 0
| 0
| 0
| 70,730
| 58,103
| 68,197
| 50,756
| 50,843
| 46,727
| 3,500
| 15,000
| 3,000
| 2,000
| 1,840
| 1,798
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
30,000
| 26
| 0
| 0
| 0
| 0
| 0
| 0
| 30,244
| 29,640
| 30,451
| 29,391
| 30,042
| 28,436
| 1,701
| 1,600
| 1,500
| 1,095
| 1,500
| 2,000
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
130,000
| 27
| 1
| -1
| 0
| -1
| -1
| 0
| 0
| 1,386
| 5,275
| 198
| 3,992
| 8,893
| 1,386
| 4,000
| 198
| 3,992
| 5,000
| 40,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
50,000
| 27
| 1
| 2
| 0
| 0
| 0
| 2
| 35,905
| 35,028
| 36,373
| 37,165
| 39,539
| 40,454
| 0
| 1,908
| 1,700
| 3,000
| 1,700
| 1,700
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
240,000
| 27
| -2
| -2
| -1
| 2
| 0
| 0
| 4,400
| 0
| 1,129
| 1,129
| 1,971
| 2,059
| 0
| 1,129
| 0
| 1,000
| 496
| 1,000
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
|
230,000
| 49
| -2
| -2
| -2
| -2
| -2
| -2
| 1,034
| 299
| 8,994
| 1,796
| 3,970
| 7,214
| 299
| 8,997
| 1,808
| 3,970
| 7,214
| 3,684
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
80,000
| 47
| -1
| 2
| 2
| -1
| -1
| -1
| 4,166
| 3,908
| 0
| 3,521
| 0
| 14,206
| 0
| 0
| 3,521
| 0
| 14,206
| 7,500
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
260,000
| 31
| 0
| 0
| 0
| 0
| 0
| 0
| 3,113
| 3,117
| 2,924
| 2,784
| 2,413
| 902
| 1,300
| 1,100
| 1,000
| 300
| 0
| 1,261
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
130,000
| 25
| -1
| -1
| -1
| -1
| -1
| -1
| 1,088
| 1,521
| 6,042
| 1,085
| 947
| 495
| 1,521
| 6,044
| 1,619
| 947
| 495
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
260,000
| 42
| 0
| 0
| 0
| 0
| 0
| 0
| 204,017
| 182,048
| 63,074
| 262,317
| 201,344
| 191,829
| 8,000
| 7,100
| 200,140
| 6,500
| 6,000
| 6,500
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
|
310,000
| 28
| 0
| 0
| 0
| 0
| 0
| 0
| 106,468
| 108,955
| 112,374
| 54,762
| 58,189
| 57,170
| 8,000
| 6,000
| 5,000
| 5,000
| 4,000
| 5,000
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
110,000
| 26
| 0
| 0
| 0
| 0
| 0
| 0
| 45,028
| 46,276
| 47,527
| 48,735
| 49,925
| 50,967
| 2,000
| 2,000
| 2,000
| 2,000
| 2,000
| 2,500
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
500,000
| 49
| -1
| -1
| -1
| 2
| -1
| 0
| 396
| 396
| 5,792
| 396
| 792
| 396
| 396
| 5,792
| 0
| 792
| 0
| 5,857
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
300,000
| 47
| -1
| -1
| -1
| -1
| -1
| -1
| 514
| 2,392
| 148
| 148
| 148
| 0
| 2,392
| 148
| 148
| 148
| 0
| 747
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
20,000
| 53
| 0
| 0
| 0
| 0
| 0
| 0
| 12,197
| 13,292
| 14,362
| 14,821
| 15,143
| 15,737
| 1,600
| 1,600
| 1,000
| 710
| 1,000
| 1,000
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
80,000
| 26
| 1
| -2
| -2
| -2
| -2
| -2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
210,000
| 31
| 0
| 0
| 0
| 0
| 0
| 0
| 157,544
| 160,437
| 129,247
| 119,579
| 120,318
| 107,122
| 7,505
| 7,072
| 4,009
| 8,000
| 10,000
| 12,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
260,000
| 63
| 0
| 0
| 0
| 0
| 0
| 2
| 261,326
| 264,126
| 244,115
| 248,831
| 263,528
| 258,973
| 9,166
| 9,001
| 9,061
| 19,155
| 1
| 9,858
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
20,000
| 36
| 0
| 0
| 0
| 0
| 0
| 0
| 18,958
| 19,427
| 19,021
| 19,449
| 19,162
| 0
| 1,500
| 2,000
| 1,200
| 413
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
10,000
| 41
| 1
| 4
| 3
| 2
| 0
| 0
| 10,711
| 10,399
| 10,092
| 9,700
| 10,000
| 9,900
| 0
| 0
| 0
| 302
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
180,000
| 25
| 0
| 0
| 0
| 0
| 0
| 0
| 120,583
| 97,341
| 87,773
| 89,547
| 91,639
| 93,991
| 3,507
| 3,201
| 3,200
| 3,500
| 4,000
| 3,704
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
430,000
| 32
| 1
| -1
| -1
| -2
| -2
| -2
| 0
| 2,500
| 0
| 0
| 0
| 0
| 2,500
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
360,000
| 37
| -1
| -1
| -1
| 0
| 0
| -1
| 4,644
| 1,417
| 3,588
| 2,589
| 1,549
| 1,278
| 1,417
| 3,588
| 0
| 0
| 1,278
| 1,998
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
50,000
| 42
| 0
| 0
| 0
| 0
| 0
| 0
| 38,516
| 28,604
| 28,089
| 27,864
| 27,173
| 27,457
| 1,754
| 1,500
| 1,500
| 1,000
| 1,100
| 1,208
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
260,000
| 46
| -2
| -2
| -2
| -2
| -2
| -2
| 0
| 135
| 0
| 0
| 109
| 945
| 135
| 0
| 0
| 109
| 945
| 9,300
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
500,000
| 36
| 0
| 0
| 0
| 0
| 0
| 0
| 70,016
| 70,837
| 63,488
| 32,050
| 46,393
| 35,207
| 2,568
| 2,000
| 1,285
| 15,000
| 1,520
| 6,994
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
440,000
| 35
| -1
| 0
| 0
| -1
| -1
| -1
| 3,091
| 3,853
| 4,012
| 821
| 821
| 821
| 1,150
| 1,084
| 821
| 821
| 821
| 821
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
30,000
| 24
| -2
| -2
| -2
| -2
| -2
| -2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
20,000
| 43
| 1
| 2
| 2
| 2
| 2
| 0
| 6,216
| 7,268
| 7,009
| 8,102
| 7,136
| 5,243
| 1,307
| 0
| 1,400
| 0
| 182
| 400
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
50,000
| 43
| 0
| 0
| 0
| 0
| 0
| 0
| 50,562
| 55,032
| 50,688
| 49,739
| 18,888
| 19,290
| 6,500
| 2,112
| 1,400
| 700
| 700
| 800
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
140,000
| 31
| 0
| 0
| 0
| 0
| 0
| 0
| 3,886
| 4,906
| 5,233
| 6,206
| 7,140
| 8,161
| 1,100
| 1,500
| 1,100
| 1,000
| 1,100
| 1,000
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
210,000
| 54
| 0
| 0
| 0
| 0
| 0
| 0
| 197,324
| 201,977
| 205,800
| 205,590
| 98,572
| 90,521
| 8,000
| 11,000
| 6,000
| 4,098
| 3,500
| 3,000
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
440,000
| 79
| 0
| 0
| 0
| 0
| 0
| 0
| 429,309
| 437,906
| 447,326
| 447,112
| 438,187
| 447,543
| 15,715
| 16,519
| 16,513
| 15,800
| 16,531
| 15,677
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
End of preview. Expand
in Data Studio
Port of the credit-card dataset from UCI (link here). See details there and use carefully.
Basic preprocessing done by the imodels team in this notebook.
The target is the binary outcome default.payment.next.month.
Sample usage
Load the data:
from datasets import load_dataset
dataset = load_dataset("imodels/credit-card")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['default.payment.next.month'])
y = df['default.payment.next.month'].values
Fit a model:
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
Evaluate:
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['default.payment.next.month'])
y_test = df['default.payment.next.month'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
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