This data reports predictors and the result of credit card applications. Its attribute names and values have been changed to symbols to protect confidentiality.
Format
A data frame containing 690 cases (rows) and 15 variables (columns).
- c.1
categorical: b, a
- c.2
continuous
- c.3
continuous
- c.4
categorical: u, y, l, t
- c.5
categorical: g, p, gg
- c.6
categorical: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff
- c.7
categorical: v, h, bb, j, n, z, dd, ff, o
- c.8
continuous
- c.9
categorical: t, f
- c.10
categorical: t, f
- c.11
continuous
- c.12
categorical: t, f
- c.13
categorical: g, p, s
- c.14
continuous
- c.15
continuous
- crit
Criterion: Credit approval.
Values:
TRUE(+) vs.FALSE(-) (44.5% vs. 55.5%).
Details
This dataset contains a mix of attributes – continuous, nominal with small sample sizes, and nominal with larger sample sizes. There are also a few missing values.
We made the following enhancements to the original data for improved usability:
Any missing values, denoted as "?" in the dataset, were transformed into
NAvalues.Binary factor variables with exclusive "t" and "f" values were converted to logical vectors (
TRUE/FALSE).
Other than that, the data remains consistent with the original dataset.
See also
Other datasets:
blood,
breastcancer,
car,
contraceptive,
fertility,
forestfires,
heart.cost,
heart.test,
heart.train,
heartdisease,
iris.v,
mushrooms,
sonar,
titanic,
voting,
wine