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This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE16) (AGI100) (AFNLWGT1) (HRSWK0)). The prediction task is to determine whether a person makes over 50K a year. Description of fnlwgt (final weight) The weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: A single cell estimate of the population 16+ for each state. Controls for Hispanic Origin by age and sex. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state. Relevant papers Ron Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996. (PDF)

15 features

agenumeric73 unique values
0 missing
workclassstring8 unique values
1836 missing
fnlwgtnumeric21648 unique values
0 missing
educationstring16 unique values
0 missing
education.numnumeric16 unique values
0 missing
marital.statusstring7 unique values
0 missing
occupationstring14 unique values
1843 missing
relationshipstring6 unique values
0 missing
racestring5 unique values
0 missing
sexstring2 unique values
0 missing
capital.gainnumeric119 unique values
0 missing
capital.lossnumeric92 unique values
0 missing
hours.per.weeknumeric94 unique values
0 missing
native.countrystring41 unique values
583 missing
incomestring2 unique values
0 missing

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Average class difference between consecutive instances.
Percentage of missing values.
Number of attributes divided by the number of instances.
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: income
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