creditapproval: Credit approval data

In ndphillips/FFTrees: Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Credit approval data

Description

This data reports predictors and the result of credit card applications. Its attribute names and values have been changed to symbols to protect confidentiality.

Usage

creditapproval

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 NA values.

• 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.

Source

https://archive.ics.uci.edu/ml/datasets/Credit+Approval

See Also

Other datasets: blood, breastcancer, car, contraceptive, fertility, forestfires, heart.cost, heart.test, heart.train, heartdisease, iris.v, mushrooms, sonar, titanic, voting, wine

ndphillips/FFTrees documentation built on April 25, 2024, 1:07 a.m.