 Data analytics on census data to predict the income and economic hierarchyK.G. Srinivasa, R. Sharath, S. Krishna Chaitanya, K.N. Nirupam and B.J. Sowmya International Journal of Data Analysis Techniques and Strategies, 2018, vol. 10, issue 3, 223-240 Abstract: The US Census Bureau conducts the American Community Survey generating a massive dataset with millions of data points. The rich dataset contains detailed information of approximately 3.5 million households about who they are and how they live including ancestry, education, work, transportation, internet use and residency. This enormous data encourages the need to know more about the population and to derive insight. The ever demanding requirement in exposing the subtlety in case of economic issues is the motivation behind to construe meaningful conclusions in income domain. Hence the focus is to concentrate on bringing out unique insights into the financial status of the people living in the country. These conclusions delineated might aid in delivering wiser decisions in regard to economic growth of the country. Using relevant attributes, demographic graphs are plotted, aiding the conclusions drawn. Also classifications into various economic classes are done using well known classifiers. Keywords: index terms-demographic graphs; Benford's law; income; K-means clustering; Naive Bayes classifier. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:10:y:2018:i:3:p:223-240 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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