Nominal Variables

In data science, nominal variables are variables that have two or more categories with no intrinsic order or ranking. Nominal variables are used to label, identify, or categorize data. Examples of nominal variables include gender, race, religion, and type of car. Nominal variables are often used in statistical analysis to determine the frequency of each category and to compare the distribution of categories between different groups. Nominal variables are also used in machine learning algorithms to classify data into different categories based on their features.
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