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Datasets:
vitaliykinakh
/
heloc

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
Libraries:
Datasets
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Auto-converted to Parquet
RiskPerformance
stringclasses
2 values
ExternalRiskEstimate
int64
-9
93
MSinceOldestTradeOpen
int64
-9
789
MSinceMostRecentTradeOpen
int64
-9
383
AverageMInFile
int64
-9
383
NumSatisfactoryTrades
int64
-9
79
NumTrades60Ever2DerogPubRec
int64
-9
19
NumTrades90Ever2DerogPubRec
int64
-9
19
PercentTradesNeverDelq
int64
-9
100
MSinceMostRecentDelq
int64
-9
83
MaxDelq2PublicRecLast12M
int64
-9
9
MaxDelqEver
int64
-9
8
NumTotalTrades
int64
-9
104
NumTradesOpeninLast12M
int64
-9
19
PercentInstallTrades
int64
-9
100
MSinceMostRecentInqexcl7days
int64
-9
24
NumInqLast6M
int64
-9
66
NumInqLast6Mexcl7days
int64
-9
66
NetFractionRevolvingBurden
int64
-9
232
NetFractionInstallBurden
int64
-9
196
NumRevolvingTradesWBalance
int64
-9
27
NumInstallTradesWBalance
int64
-9
23
NumBank2NatlTradesWHighUtilization
int64
-9
16
PercentTradesWBalance
int64
-9
100
Good
81
6
1
86
32
0
0
100
-7
7
8
3
3
33
5
1
1
18
92
5
2
1
47
Good
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
Bad
61
92
9
86
25
0
0
96
6
4
6
30
1
44
1
2
2
65
86
5
3
3
100
Good
87
210
19
110
8
0
0
100
-7
7
8
10
0
50
18
0
0
19
-8
2
1
0
60
Bad
77
137
20
68
17
0
0
100
-7
7
8
18
0
17
0
5
4
48
-8
2
1
1
60
Good
83
262
3
115
19
0
0
100
-7
7
8
20
1
45
2
3
3
31
66
2
3
0
56
Bad
82
367
1
90
26
0
0
96
40
6
6
28
3
14
3
2
2
1
-8
5
-8
0
45
Bad
77
140
2
73
15
0
0
88
12
6
6
17
2
53
0
1
1
17
64
2
4
-8
67
Bad
67
194
5
80
17
0
0
94
2
4
6
17
1
41
3
1
1
30
49
1
4
1
71
Good
73
223
23
99
22
0
0
91
9
4
6
23
0
35
4
1
1
6
65
6
2
0
80
Bad
73
379
1
129
23
4
3
86
42
6
2
29
1
52
0
3
3
33
-8
7
2
1
69
Good
70
142
2
56
17
1
0
89
34
6
5
19
2
37
2
1
1
52
63
4
3
2
67
Bad
67
124
2
53
18
0
0
89
6
4
6
20
1
47
2
3
3
30
58
5
2
1
100
Bad
80
446
20
103
27
0
0
100
-7
7
8
28
0
46
1
2
2
0
10
1
4
1
50
Bad
59
244
4
76
27
1
0
80
6
4
5
30
2
40
0
2
2
88
54
7
4
3
93
Bad
58
231
13
73
20
2
2
90
4
4
6
23
0
24
0
2
2
54
39
2
4
1
60
Bad
69
362
5
125
16
0
0
82
25
6
6
17
1
47
0
2
2
63
-8
4
1
3
71
Good
87
81
4
126
14
0
0
100
-7
7
8
4
1
14
-7
2
2
4
-8
3
1
0
40
Bad
75
278
3
93
17
0
0
100
-7
7
8
17
4
47
2
2
2
43
-8
3
-8
1
100
Bad
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
Good
71
131
3
61
35
0
0
100
-7
7
8
37
5
50
-7
5
5
43
94
6
2
1
89
Good
75
134
3
63
28
1
0
97
80
6
5
34
2
21
2
5
5
16
-8
3
1
0
67
Good
57
382
6
125
36
3
0
62
28
6
5
45
4
27
0
2
2
72
93
10
5
3
76
Good
86
279
9
115
16
0
0
94
59
6
6
16
1
13
0
3
3
5
-8
1
-8
0
20
Bad
55
176
14
68
17
1
1
89
2
4
6
20
0
42
0
3
3
84
44
6
2
4
80
Bad
57
47
9
35
5
1
0
50
16
6
5
10
1
30
0
2
2
66
70
2
2
0
57
Bad
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
Good
82
311
17
133
18
0
0
100
-7
7
8
21
0
22
-7
0
0
34
-8
4
2
1
60
Bad
60
188
31
136
12
1
0
65
4
4
5
17
0
47
0
3
3
51
54
4
2
1
100
Bad
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
Bad
58
139
16
87
14
1
0
75
2
3
5
16
0
50
0
2
2
84
-8
7
1
1
100
Bad
58
151
12
50
9
2
2
67
4
4
6
9
0
44
0
0
0
65
86
5
2
2
100
Bad
70
52
17
42
8
0
0
100
-7
7
8
8
0
38
-7
0
0
95
-8
3
1
2
67
Good
69
155
10
85
26
0
0
100
-7
7
8
26
1
12
1
2
2
54
81
12
1
5
68
Good
68
160
2
70
63
0
0
97
13
6
6
71
5
48
20
0
0
35
105
11
7
0
75
Good
84
213
9
76
17
0
0
100
-7
7
8
17
1
18
16
0
0
35
53
2
2
0
57
Bad
58
71
4
44
4
1
0
60
57
6
5
5
2
60
0
0
0
99
-8
1
1
1
100
Good
87
190
16
139
28
0
0
100
-7
7
8
29
0
34
7
0
0
19
-8
3
1
0
50
Good
87
133
7
67
11
0
0
100
-7
7
8
2
1
55
8
0
0
0
82
0
2
0
40
Bad
60
180
2
61
12
8
6
67
30
6
2
18
3
44
-7
0
0
46
97
4
1
2
86
Bad
55
336
5
111
17
2
1
74
1
0
2
20
2
32
-7
0
0
39
-8
5
2
0
88
Good
87
112
5
67
22
0
0
100
-7
7
8
22
2
55
5
0
0
3
92
1
3
0
44
Bad
68
195
4
67
22
1
0
87
34
6
5
23
3
52
2
3
3
74
88
3
3
2
75
Bad
77
156
17
71
19
0
0
95
6
4
6
22
0
32
8
0
0
1
84
2
4
0
86
Good
65
261
2
93
14
2
2
87
27
6
6
17
2
47
0
0
0
29
96
4
2
0
67
Bad
64
99
7
43
8
0
0
100
-7
7
8
8
2
38
0
4
3
76
89
5
3
1
100
Bad
77
252
4
54
24
0
0
100
-7
7
8
37
3
42
3
2
2
15
78
4
5
1
64
Bad
60
206
9
40
8
2
2
88
-8
6
6
9
2
88
-7
0
0
99
81
1
5
1
100
Bad
62
132
7
69
31
0
0
100
-7
7
8
33
2
24
0
1
1
87
96
7
4
4
69
Good
77
242
15
97
23
0
0
100
-7
7
8
23
0
4
0
0
0
44
-8
5
1
2
50
Bad
63
161
5
45
37
0
0
100
-7
7
8
39
6
18
0
1
1
41
84
20
2
2
96
Bad
60
205
4
74
32
0
0
88
4
4
6
34
5
59
-7
4
4
32
62
4
3
0
80
Bad
63
242
2
68
25
0
0
100
-7
7
8
26
1
19
-7
3
3
73
-8
12
1
5
87
Bad
58
108
4
49
11
2
1
57
6
4
2
14
3
71
0
4
3
0
74
1
4
-8
83
Bad
79
340
4
80
52
0
0
96
10
4
6
77
2
13
6
0
0
2
-8
2
1
0
12
Bad
63
445
3
123
35
4
3
90
1
0
2
43
3
20
1
5
5
11
79
9
3
0
65
Good
73
178
4
73
14
0
0
100
-7
7
8
14
2
50
15
0
0
74
93
6
2
1
100
Good
71
236
1
76
18
0
0
100
-7
7
8
20
4
60
1
4
4
84
-8
3
7
3
91
Good
91
262
19
101
9
0
0
100
-7
7
8
9
0
0
19
0
0
8
-8
2
-8
0
50
Good
90
113
104
107
4
0
0
100
-7
7
8
4
0
0
0
1
1
5
-8
2
-8
0
75
Good
89
125
48
69
7
0
0
100
-7
7
8
4
0
14
-8
0
0
4
-8
2
1
0
100
Bad
61
198
6
56
20
0
0
100
-7
7
8
21
2
5
0
1
1
88
-8
12
1
8
87
Bad
78
132
32
72
15
0
0
94
47
6
6
17
0
59
10
0
0
0
-8
0
2
0
40
Bad
67
-8
7
68
34
0
0
100
-7
7
8
39
3
13
-7
5
5
90
40
13
4
9
89
Good
76
167
16
81
23
0
0
100
-7
7
8
24
0
17
-7
0
0
35
70
4
2
2
67
Good
82
127
4
69
27
0
0
100
-7
7
8
28
3
18
0
3
3
25
99
6
3
1
50
Bad
63
76
5
40
20
1
1
86
-8
5
6
22
2
27
0
2
1
94
45
14
3
1
100
Good
75
187
6
70
32
0
0
100
-7
7
8
32
5
28
0
0
0
20
81
6
3
1
47
Bad
76
42
18
27
7
0
0
100
-7
7
8
7
0
71
0
1
1
30
-8
1
3
0
80
Good
80
136
15
63
11
0
0
91
21
6
6
11
0
36
-7
1
1
19
54
2
3
0
71
Good
79
162
8
66
28
0
0
90
19
6
6
30
4
50
2
1
1
14
84
4
2
0
75
Bad
78
342
3
134
10
0
0
80
16
6
6
10
1
30
0
1
1
39
-8
3
1
0
80
Bad
61
146
3
58
35
0
0
63
6
4
6
44
4
45
6
0
0
27
87
4
3
2
64
Good
82
247
21
99
28
0
0
100
-7
7
8
32
0
32
15
0
0
20
30
5
3
1
67
Bad
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
-9
Bad
44
-8
40
131
57
3
1
68
0
0
5
63
0
32
-7
1
1
58
13
17
3
4
79
Bad
54
179
8
81
21
0
0
62
0
4
6
21
3
14
-7
2
2
92
86
11
3
10
94
Good
73
304
18
86
14
1
1
100
-7
7
8
15
0
43
-7
0
0
0
2
1
2
0
43
Bad
85
325
11
79
39
0
0
98
-7
5
7
41
1
29
0
2
2
19
80
3
5
0
57
Good
64
130
1
33
13
0
0
100
-7
7
8
14
6
0
4
1
1
64
-8
7
-8
3
78
Bad
66
286
1
114
45
0
0
93
68
6
6
46
1
20
0
0
0
67
-8
12
4
2
71
Good
85
288
6
100
25
0
0
100
-7
7
8
25
2
24
0
0
0
10
57
3
2
0
50
Bad
46
176
15
83
11
1
0
57
1
3
5
14
0
50
0
2
2
104
-8
3
1
2
100
Good
62
89
22
61
24
0
0
92
31
6
6
25
0
36
11
0
0
70
-8
4
1
2
63
Bad
75
-8
7
24
5
0
0
100
-7
7
8
0
2
0
6
0
0
3
-8
2
-8
0
40
Bad
66
183
4
64
19
4
3
82
18
6
2
22
2
50
0
0
0
85
85
4
7
1
92
Good
87
306
49
122
14
0
0
100
-7
7
8
14
0
36
13
0
0
3
-8
2
-8
0
67
Bad
70
291
8
88
14
0
0
100
-7
7
8
8
1
29
-7
0
0
100
67
5
2
4
100
Bad
60
168
5
86
11
0
0
73
5
4
6
11
2
18
-8
0
0
63
68
4
1
1
71
Bad
65
121
1
41
20
2
2
88
9
0
2
26
3
27
0
5
5
15
52
3
4
0
47
Good
85
216
3
72
28
0
0
93
-8
5
6
28
2
11
0
1
1
3
37
7
2
0
60
Good
74
306
4
91
34
0
0
97
60
6
6
37
2
26
-7
2
2
12
78
7
4
0
67
Good
86
304
103
167
9
0
0
100
-7
7
8
9
0
22
-8
0
0
27
20
1
1
0
67
Bad
60
-8
10
110
35
2
2
95
2
4
6
37
1
27
6
0
0
79
74
9
2
7
73
Bad
64
222
6
95
16
1
0
82
6
4
5
17
2
47
0
1
1
36
92
5
2
1
70
Good
66
311
9
84
32
1
1
100
-7
7
8
33
3
39
-7
0
0
48
64
9
5
1
74
Bad
57
138
18
66
11
2
1
71
23
6
3
14
0
36
0
0
0
41
-8
4
1
0
71
Good
71
145
3
53
33
0
0
100
-7
7
8
33
4
33
0
6
6
31
99
4
3
0
39
Bad
85
-8
5
79
5
0
0
100
-7
7
8
0
1
40
1
2
2
19
-8
3
-8
1
100
Bad
82
69
21
41
10
0
0
100
-7
7
8
10
0
10
0
1
1
13
-8
1
1
0
67
End of preview. Expand in Data Studio

HELOC (Home Equity Line of Credit)

The HELOC dataset from FICO. Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price). The customers in this dataset have requested a credit line in the range of $5,000 - $150,000. The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.

Configurations and tasks

Configuration Task Description
RiskPerformance Binary classification Will the customer default?

Source

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Size of downloaded dataset files:
669 kB
Size of the auto-converted Parquet files:
341 kB
Number of rows:
10,459

Models trained or fine-tuned on vitaliykinakh/heloc