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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
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12,768
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0
0
0
0
0
0
0
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0
1
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0
1
0
0
0
0
0
0
1
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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
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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
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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
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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
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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
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133,131
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1
0
0
1
0
0
0
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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
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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
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27,898
27,315
27,709
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2,257
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1,000
2,000
1
0
0
0
1
0
0
0
0
0
0
1
0
0
230,000
26
-2
-2
-2
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416
371
416
416
566
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371
461
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566
416
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1
0
0
1
0
0
0
0
0
0
0
1
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0
50,000
35
0
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0
0
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3,000
3,019
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2,000
1
0
0
0
1
0
0
0
0
0
0
1
0
0
80,000
38
0
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0
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0
19,277
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0
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1
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0
1
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80,000
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20,000
20,000
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2,089
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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
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29,282
29,270
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2,403
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588
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574
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1
0
0
1
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0
0
0
0
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1
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0
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280,000
30
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380
380
380
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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
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0
0
0
2,498
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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
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1
0
0
1
0
0
0
0
0
1
0
0
0
30,000
23
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0
2
2
2
-1
19,321
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25,809
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3,316
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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
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46,272
49,050
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1,900
0
3,700
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0
1
0
0
0
1
0
0
0
0
0
1
0
1
220,000
33
-1
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-1
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492
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1
0
1
0
0
0
0
0
1
0
0
0
0
20,000
27
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0
0
0
-1
2
20,443
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38,730
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690
150
1,558
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400
1,290
0
780
1
0
0
0
0
1
0
0
0
0
0
1
0
0
50,000
47
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0
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0
0
0
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1
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1
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0
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0
1
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0
80,000
23
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-2
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1,600
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0
1
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1
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0
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1
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0
110,000
24
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2
0
0
0
0
115,527
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4,000
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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
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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
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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
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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
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2,333
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2,361
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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

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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