{ "data_id": "4535", "name": "Census-Income", "exact_name": "Census-Income", "version": 1, "version_label": null, "description": "**Author**: U.S. Census Bureau http:\/\/www.census.gov\/ United States Department of Commerce \n**Source**: UCI\n**Please cite**: Please refer to the Machine Learning Repository's citation policy \n\nSource:\n\nOriginal Owner: \n\nU.S. Census Bureau \nhttp:\/\/www.census.gov\/ \nUnited States Department of Commerce \n\nDonor: \n\nTerran Lane and Ronny Kohavi \nData Mining and Visualization \nSilicon Graphics. \nterran '@' ecn.purdue.edu, ronnyk '@' sgi.com \n\n\n\n\nData Set Information:\n\nThis 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. \n\nThe 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. \n\nOne instance per line with comma delimited fields. There are 199523 instances in the data file and 99762 in the test file. \n\nThe data was split into train\/test in approximately 2\/3, 1\/3 proportions using MineSet's MIndUtil mineset-to-mlc. \n\n\n\nAttribute Information:\n\nMore 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: \n\nage AAGE \nclass of worker ACLSWKR \nindustry code ADTIND \noccupation code ADTOCC \nadjusted gross income AGI \neducation AHGA \nwage per hour AHRSPAY \nenrolled in edu inst last wk AHSCOL \nmarital status AMARITL \nmajor industry code AMJIND \nmajor occupation code AMJOCC \nmace ARACE \nhispanic Origin AREORGN \nsex ASEX \nmember of a labor union AUNMEM \nreason for unemployment AUNTYPE \nfull or part time employment stat AWKSTAT \ncapital gains CAPGAIN \ncapital losses CAPLOSS \ndivdends from stocks DIVVAL \nfederal income tax liability FEDTAX \ntax filer status FILESTAT \nregion of previous residence GRINREG \nstate of previous residence GRINST \ndetailed household and family stat HHDFMX \ndetailed household summary in household HHDREL \ninstance weight MARSUPWT \nmigration code-change in msa MIGMTR1 \nmigration code-change in reg MIGMTR3 \nmigration code-move within reg MIGMTR4 \nlive in this house 1 year ago MIGSAME \nmigration prev res in sunbelt MIGSUN \nnum persons worked for employer NOEMP \nfamily members under 18 PARENT \ntotal person earnings PEARNVAL \ncountry of birth father PEFNTVTY \ncountry of birth mother PEMNTVTY \ncountry of birth self PENATVTY \ncitizenship PRCITSHP \ntotal person income PTOTVAL \nown business or self employed SEOTR \ntaxable income amount TAXINC \nfill inc questionnaire for veteran's admin VETQVA \nveterans benefits VETYN \nweeks worked in year WKSWORK \n\nNote 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. \n\n\n\nRelevant Papers:\n\nN\/A\n\n \nPapers That Cite This Data Set1:\n\nEibe Frank and Geoffrey Holmes and Richard Kirkby and Mark A. Hall. Racing Committees for Large Datasets. Discovery Science. 2002. [View Context].\n\nNikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001. [View Context].\n\nStephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001. [View Context].\n\nMasahiro Terabe and Takashi Washio and Hiroshi Motoda. The Effect of Subsampling Rate on S 3 Bagging Performance. Mitsubishi Research Institute. [View Context].\n\n\n\n\nCitation Request:\n\nPlease refer to the Machine Learning Repository's citation policy", "format": "ARFF", "uploader": "Hilda Fabiola Bernard", "uploader_id": 874, "visibility": "public", "creator": "\"U.S. Census Bureau http:\/\/www.census.gov\/ United States Department of Commerce\"", "contributor": null, "date": "2016-02-16 15:42:48", "update_comment": null, "last_update": "2016-02-16 15:42:48", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/1798107\/phprAeXmK", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Census-Income", "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 num " ], "weight": 5 }, "qualities": { "NumberOfInstances": 299285, "NumberOfFeatures": 42, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 13, "NumberOfSymbolicFeatures": 29, "Quartile1SkewnessOfNumericAtts": 0.29140129655832414, "MeanMeansOfNumericAtts": 203.24428145541845, "Quartile1StdDevOfNumericAtts": 1.6076260100109963, "AutoCorrelation": null, "MeanMutualInformation": null, "Quartile2AttributeEntropy": null, "ClassEntropy": null, "MeanNoiseToSignalRatio": null, "Quartile2KurtosisOfNumericAtts": -0.5259137182167595, "Dimensionality": 0.00014033446380540287, "MeanNominalAttDistinctValues": 13.724137931034482, "Quartile2MeansOfNumericAtts": 34.5389979451024, "EquivalentNumberOfAtts": null, "MeanSkewnessOfNumericAtts": 5.277902798400274, "Quartile2MutualInformation": null, "MajorityClassPercentage": null, "MeanStdDevOfNumericAtts": 633.030231536132, 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"type": "nominal", "distinct": "2", "missing": "0", "distr": [ [ " Female", " Male" ], [ [ "151807", "3968" ], [ "128910", "14600" ] ] ] }, { "name": "V14", "index": "13", "type": "nominal", "distinct": "3", "missing": "0", "distr": [ [ " No", " Not in universe", " Yes" ], [ [ "21356", "2712" ], [ "255499", "15243" ], [ "3862", "613" ] ] ] }, { "name": "V15", "index": "14", "type": "nominal", "distinct": "6", "missing": "0", "distr": [ [ " Job leaver", " Job loser - on layoff", " New entrant", " Not in universe", " Other job loser", " Re-entrant" ], [ [ "853", "31" ], [ "1448", "36" ], [ "641", "2" ], [ "271774", "18311" ], [ "2986", "160" ], [ "3015", "28" ] ] ] }, { "name": "V16", "index": "15", "type": "nominal", "distinct": "8", "missing": "0", "distr": [ [ " Children or Armed Forces", " Full-time schedules", " Not in labor force", " PT for econ reasons usually FT", " PT for econ reasons usually PT", " PT for non-econ reasons usually FT", " Unemployed full-time", " Unemployed part- 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"min": "0", "max": "2", "mean": "0", "stdev": "1" }, { "name": "V38", "index": "37", "type": "nominal", "distinct": "3", "missing": "0", "distr": [ [ " No", " Not in universe", " Yes" ], [ [ "2090", "331" ], [ "278061", "18213" ], [ "566", "24" ] ] ] }, { "name": "V39", "index": "38", "type": "numeric", "distinct": "3", "missing": "0", "min": "0", "max": "2", "mean": "2", "stdev": "1" }, { "name": "V40", "index": "39", "type": "numeric", "distinct": "53", "missing": "0", "min": "0", "max": "52", "mean": "23", "stdev": "24" }, { "name": "V41", "index": "40", "type": "numeric", "distinct": "2", "missing": "0", "min": "94", "max": "95", "mean": "94", "stdev": "1" }, { "name": "V42", "index": "41", "type": "nominal", "distinct": "2", "missing": "0", "distr": [ [ " - 50000.", " 50000+." ], [ [ "280717", "0" ], [ "0", "18568" ] ] ] } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 1, "nr_of_downloads": 5, "total_downloads": 9, "reach": 6, "reuse": 5, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 5 }