Categorical Variables
In data science, categorical variables are variables that take on a limited, and usually fixed, number of possible values. These values are typically used to label or group data, and can be either nominal or ordinal. Nominal categorical variables have no inherent order or ranking, and examples include gender, race, and type of car. Ordinal categorical variables have a natural ordering, and examples include education level (e.g. high school, college, graduate degree) and income level (e.g. low, medium, high). Categorical variables are important in data analysis because they can provide insights into patterns and relationships within the data, and can be used to create predictive models.
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