The objective of this case study is to fit and compare three different binary classifiers to predict whether an individual earns more than USD 50,000 (50K) or less in a year using the 1994 US Census
Data sourced from the Machine Learning Repository (Lichman, 2013).
The descriptive features include 4 numeric and 7 nominal categorical features.
The target feature has two classes defined as "<=50K" and ">50K" respectively.
The full dataset contains about 45K observations.
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By purchasing this solution you'll be able to access the following files:
Solution.docx and Solution.Rmd.