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In simple words feature column are bridge between raw data and estimator or model. Some real-world features (such as, longitude) are numerical, but many are not.
Often in real-time, data includes the text columns, which are repetitive. Features like gender, country, and codes are always repetitive. These are the examples for categorical data. Categorical variables can take on only a limited, and usually fixed number of possible values. Categorical are a Pandas' data type.
Examples of categorical variables are race, sex, age group, and educational level. While the latter two variables may also be considered in a numerical manner by using exact values for age and highest grade completed, it is often more informative to categorize such variables into a relatively small number of groups.
In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.
Also known as categorical variables, qualitative variables are variables with no natural sense of ordering. They are therefore measured on a nominal scale. For instance, hair color (Black, Brown, Gray, Red, Yellow) is a qualitative variable, as is name (Adam, Becky, Christina, Dave. . .).
Categorical data: Categorical data represent characteristics such as a person's gender, marital status, hometown, or the types of movies they like. Categorical data can take on numerical values (such as 1 indicating male and 2 indicating female), but those numbers don't have mathematical meaning.
A numerical variable is a variable where the measurement or number has a numerical meaning. For example, total rainfall measured in inches is a numerical value, heart rate is a numerical value, number of cheeseburgers consumed in an hour is a numerical value.
Introduction. Categorical Data is the data that generally takes a limited number of possible values. Also, the data in the category need not be numerical, it can be textual in nature. All machine learning models are some kind of mathematical model that need numbers to work with.
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