Predicting Income Status

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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