rebmix (version 2.12.0)

adult: Adult Dataset

Description

The adult dataset containing 48842 instances with 16 continuous, binary and discrete variables was extracted from the census bureau database. Extraction was done by Barry Becker from the 1994 census bureau database.

Usage

data("adult")

Format

adult is a data frame with 48842 cases (rows) and 16 variables (columns) named:

1. Type binary train or test.

2. Age continuous.

3. Workclass one of the 8 discrete values private, self-emp-not-inc, self-emp-inc, federal-gov, local-gov, state-gov, without-pay or never-worked.

4. Fnlwgt stands for continuous final weight.

5. Education one of the 16 discrete values bachelors, some-college, 11th, hs-grad, prof-school, assoc-acdm, assoc-voc, 9th, 7th-8th, 12th, masters, 1st-4th, 10th, doctorate, 5th-6th or preschool.

6. Education.Num continuous.

7. Marital.Status one of the 7 discrete values married-civ-spouse, divorced, never-married, separated, widowed, married-spouse-absent or married-af-spouse.

8. Occupation one of the 14 discrete values tech-support, craft-repair, other-service, sales, exec-managerial, prof-specialty, handlers-cleaners, machine-op-inspct, adm-clerical, farming-fishing, transport-moving, priv-house-serv, protective-serv or armed-forces.

9. Relationship one of the 6 discrete values wife, own-child, husband, not-in-family, other-relative or unmarried.

10. Race one of the 5 discrete values white, asian-pac-islander, amer-indian-eskimo, other or black.

11. Sex binary female or male.

12. Capital.Gain continuous.

13. Capital.Loss continuous.

14. Hours.Per.Week continuous.

15. Native.Country one of the 41 discrete values united-states, cambodia, england, puerto-rico, canada, germany, outlying-us(guam-usvi-etc), india, japan, greece, south, china, cuba, iran, honduras, philippines, italy, poland, jamaica, vietnam, mexico, portugal, ireland, france, dominican-republic, laos, ecuador, taiwan, haiti, columbia, hungary, guatemala, nicaragua, scotland, thailand, yugoslavia, el-salvador, trinadad&tobago, peru, hong or holand-netherlands.

16. Income binary <=50k or >50k.

References

A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml.

Examples

# NOT RUN {
data("adult")

# Find complete cases.

adult <- adult[complete.cases(adult),]

# Show level attributes for binary and discrete variables.

levels(adult[["Type"]])
levels(adult[["Workclass"]])
levels(adult[["Education"]])
levels(adult[["Marital.Status"]])
levels(adult[["Occupation"]])
levels(adult[["Relationship"]])
levels(adult[["Race"]])
levels(adult[["Sex"]])
levels(adult[["Native.Country"]])
levels(adult[["Income"]])
# }