Datasets:
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 Dataset Viewer.
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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