Data
ACSIncome

ACSIncome

active ARFF Public Domain (CC0) Visibility: public Uploaded 10-01-2022 by Kevin Spiekermann
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  • Computer Systems Machine Learning
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The ACSIncome dataset is one of five datasets created by Ding et al. as an improved alternative to the popular UCI Adult dataset. The authors compiled data from the American Community Survey (ACS) Public Use Microdata Sample (PUMS). Data is provided for all 50 states and Puerto Rico. This upload represents the raw data from only 2018. The data contains 1,664,500 rows, 10 features, and 1 target variable. An additional column for the state code is provided for convenience. All columns are described in the original publication (https://arxiv.org/pdf/2108.04884.pdf) as well as in the PUMS Data Dictionary ( https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2018.pdf). Additional detail can also be found on the author's GitHub: https://github.com/zykls/folktables/

12 features

AGEPnumeric80 unique values
0 missing
COWnumeric8 unique values
0 missing
SCHLnumeric24 unique values
0 missing
MARnumeric5 unique values
0 missing
OCCPnumeric529 unique values
0 missing
POBPnumeric224 unique values
0 missing
RELPnumeric18 unique values
0 missing
WKHPnumeric99 unique values
0 missing
SEXnumeric2 unique values
0 missing
RAC1Pnumeric9 unique values
0 missing
STnumeric51 unique values
0 missing
PINCPnumeric18107 unique values
0 missing

19 properties

1664500
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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