Census-Income

Author: U.S. Census Bureau http://www.census.gov/ United States Department of Commerce Source: UCI Please cite: Please refer to the Machine Learning Repository's citation policy Source: Original Owner: U.S. Census Bureau http://www.census.gov/ United States Department of Commerce Donor: Terran Lane and Ronny Kohavi Data Mining and Visualization Silicon Graphics. terran '@' ecn.purdue.edu, ronnyk '@' sgi.com Data Set Information: This data set contains weighted census data extracted from the 1994 and 1995 Current Population Surveys conducted by the U.S. Census Bureau. The data contains 41 demographic and employment related variables. The instance weight indicates the number of people in the population that each record represents due to stratified sampling. To do real analysis and derive conclusions, this field must be used. This attribute should *not* be used in the classifiers. One instance per line with comma delimited fields. There are 199523 instances in the data file and 99762 in the test file. The data was split into train/test in approximately 2/3, 1/3 proportions using MineSet's MIndUtil mineset-to-mlc. Attribute Information: More information detailing the meaning of the attributes can be found in the Census Bureau's documentation To make use of the data descriptions at this site, the following mappings to the Census Bureau's internal database column names will be needed: age AAGE class of worker ACLSWKR industry code ADTIND occupation code ADTOCC adjusted gross income AGI education AHGA wage per hour AHRSPAY enrolled in edu inst last wk AHSCOL marital status AMARITL major industry code AMJIND major occupation code AMJOCC mace ARACE hispanic Origin AREORGN sex ASEX member of a labor union AUNMEM reason for unemployment AUNTYPE full or part time employment stat AWKSTAT capital gains CAPGAIN capital losses CAPLOSS divdends from stocks DIVVAL federal income tax liability FEDTAX tax filer status FILESTAT region of previous residence GRINREG state of previous residence GRINST detailed household and family stat HHDFMX detailed household summary in household HHDREL instance weight MARSUPWT migration code-change in msa MIGMTR1 migration code-change in reg MIGMTR3 migration code-move within reg MIGMTR4 live in this house 1 year ago MIGSAME migration prev res in sunbelt MIGSUN num persons worked for employer NOEMP family members under 18 PARENT total person earnings PEARNVAL country of birth father PEFNTVTY country of birth mother PEMNTVTY country of birth self PENATVTY citizenship PRCITSHP total person income PTOTVAL own business or self employed SEOTR taxable income amount TAXINC fill inc questionnaire for veteran's admin VETQVA veterans benefits VETYN weeks worked in year WKSWORK Note that Incomes have been binned at the $50K level to present a binary classification problem, much like the original UCI/ADULT database. The goal field of this data, however, was drawn from the "total person income" field rather than the "adjusted gross income" and may, therefore, behave differently than the orginal ADULT goal field. Relevant Papers: N/A Papers That Cite This Data Set1: Eibe Frank and Geoffrey Holmes and Richard Kirkby and Mark A. Hall. Racing Committees for Large Datasets. Discovery Science. 2002. [View Context]. Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001. [View Context]. Stephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001. [View Context]. Masahiro Terabe and Takashi Washio and Hiroshi Motoda. The Effect of Subsampling Rate on S 3 Bagging Performance. Mitsubishi Research Institute. [View Context]. Citation Request: Please refer to the Machine Learning Repository's citation policy