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b | 27.75 | 0.585 | y | p | cc | v | 0.25 | t | t | 2 | f | g | 00260 | 500 | + |
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b | 26.00 | 1 | u | g | q | v | 1.75 | t | f | 0 | t | g | 00280 | 0 | + |
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b | 34.92 | 2.5 | u | g | w | v | 0 | t | f | 0 | t | g | 00239 | 200 | + |
b | 34.25 | 3 | u | g | cc | h | 7.415 | t | f | 0 | t | g | 00000 | 0 | + |
b | 23.33 | 11.625 | y | p | w | v | 0.835 | t | f | 0 | t | g | 00160 | 300 | + |
b | 23.17 | 0 | u | g | cc | v | 0.085 | t | f | 0 | f | g | 00000 | 0 | + |
b | 44.33 | 0.5 | u | g | i | h | 5 | t | f | 0 | t | g | 00320 | 0 | + |
b | 35.17 | 4.5 | u | g | x | h | 5.75 | f | f | 0 | t | s | 00711 | 0 | + |
b | 43.25 | 3 | u | g | q | h | 6 | t | t | 11 | f | g | 00080 | 0 | + |
b | 56.75 | 12.25 | u | g | m | v | 1.25 | t | t | 4 | t | g | 00200 | 0 | + |
b | 31.67 | 16.165 | u | g | d | v | 3 | t | t | 9 | f | g | 00250 | 730 | + |
a | 23.42 | 0.79 | y | p | q | v | 1.5 | t | t | 2 | t | g | 00080 | 400 | + |
a | 20.42 | 0.835 | u | g | q | v | 1.585 | t | t | 1 | f | g | 00000 | 0 | + |
b | 26.67 | 4.25 | u | g | cc | v | 4.29 | t | t | 1 | t | g | 00120 | 0 | + |
b | 34.17 | 1.54 | u | g | cc | v | 1.54 | t | t | 1 | t | g | 00520 | 50,000 | + |
a | 36.00 | 1 | u | g | c | v | 2 | t | t | 11 | f | g | 00000 | 456 | + |
b | 25.50 | 0.375 | u | g | m | v | 0.25 | t | t | 3 | f | g | 00260 | 15,108 | + |
b | 19.42 | 6.5 | u | g | w | h | 1.46 | t | t | 7 | f | g | 00080 | 2,954 | + |
b | 35.17 | 25.125 | u | g | x | h | 1.625 | t | t | 1 | t | g | 00515 | 500 | + |
b | 32.33 | 7.5 | u | g | e | bb | 1.585 | t | f | 0 | t | s | 00420 | 0 | - |
b | 34.83 | 4 | u | g | d | bb | 12.5 | t | f | 0 | t | g | ? | 0 | - |
a | 38.58 | 5 | u | g | cc | v | 13.5 | t | f | 0 | t | g | 00980 | 0 | - |
b | 44.25 | 0.5 | u | g | m | v | 10.75 | t | f | 0 | f | s | 00400 | 0 | - |
b | 44.83 | 7 | y | p | c | v | 1.625 | f | f | 0 | f | g | 00160 | 2 | - |
b | 20.67 | 5.29 | u | g | q | v | 0.375 | t | t | 1 | f | g | 00160 | 0 | - |
b | 34.08 | 6.5 | u | g | aa | v | 0.125 | t | f | 0 | t | g | 00443 | 0 | - |
a | 19.17 | 0.585 | y | p | aa | v | 0.585 | t | f | 0 | t | g | 00160 | 0 | - |
b | 21.67 | 1.165 | y | p | k | v | 2.5 | t | t | 1 | f | g | 00180 | 20 | - |
b | 21.50 | 9.75 | u | g | c | v | 0.25 | t | f | 0 | f | g | 00140 | 0 | - |
b | 49.58 | 19 | u | g | ff | ff | 0 | t | t | 1 | f | g | 00094 | 0 | - |
a | 27.67 | 1.5 | u | g | m | v | 2 | t | f | 0 | f | s | 00368 | 0 | - |
b | 39.83 | 0.5 | u | g | m | v | 0.25 | t | f | 0 | f | s | 00288 | 0 | - |
a | ? | 3.5 | u | g | d | v | 3 | t | f | 0 | t | g | 00300 | 0 | - |
b | 27.25 | 0.625 | u | g | aa | v | 0.455 | t | f | 0 | t | g | 00200 | 0 | - |
b | 37.17 | 4 | u | g | c | bb | 5 | t | f | 0 | t | s | 00280 | 0 | - |
b | ? | 0.375 | u | g | d | v | 0.875 | t | f | 0 | t | s | 00928 | 0 | - |
b | 25.67 | 2.21 | y | p | aa | v | 4 | t | f | 0 | f | g | 00188 | 0 | - |
b | 34.00 | 4.5 | u | g | aa | v | 1 | t | f | 0 | t | g | 00240 | 0 | - |
a | 49.00 | 1.5 | u | g | j | j | 0 | t | f | 0 | t | g | 00100 | 27 | - |
b | 62.50 | 12.75 | y | p | c | h | 5 | t | f | 0 | f | g | 00112 | 0 | - |
b | 31.42 | 15.5 | u | g | c | v | 0.5 | t | f | 0 | f | g | 00120 | 0 | - |
b | ? | 5 | y | p | aa | v | 8.5 | t | f | 0 | f | g | 00000 | 0 | - |
b | 52.33 | 1.375 | y | p | c | h | 9.46 | t | f | 0 | t | g | 00200 | 100 | - |
b | 28.75 | 1.5 | y | p | c | v | 1.5 | t | f | 0 | t | g | 00000 | 225 | - |
a | 28.58 | 3.54 | u | g | i | bb | 0.5 | t | f | 0 | t | g | 00171 | 0 | - |
b | 23.00 | 0.625 | y | p | aa | v | 0.125 | t | f | 0 | f | g | 00180 | 1 | - |
b | ? | 0.5 | u | g | c | bb | 0.835 | t | f | 0 | t | s | 00320 | 0 | - |
a | 22.50 | 11 | y | p | q | v | 3 | t | f | 0 | t | g | 00268 | 0 | - |
a | 28.50 | 1 | u | g | q | v | 1 | t | t | 2 | t | g | 00167 | 500 | - |
b | 37.50 | 1.75 | y | p | c | bb | 0.25 | t | f | 0 | t | g | 00164 | 400 | - |
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Credit Card Dataset (Teeny-Tiny Castle)
This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.
How to Use
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/data-credit-card", split = 'train')
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