Nominal

In data science, nominal refers to a type of categorical data that represents variables with no inherent order or ranking. Nominal data is used to label variables without providing any quantitative value or order. Examples of nominal data include gender, race, religion, and political affiliation. Nominal data can be represented using numbers, but these numbers do not have any mathematical meaning. Nominal data is often analyzed using frequency tables and bar charts to show the distribution of the categories. In machine learning, nominal data is often converted into numerical data using techniques such as one-hot encoding, where each category is represented by a binary variable. This allows the data to be used in algorithms that require numerical input.
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