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Understand variables and their type in Machine Learning.

Updated: Oct 6, 2022

A variable represents one specific characteristic of the data or tells specific information about the data.

The above table represents the data. The column such as CustomerName, City, and PostalCode represents Variables. The variables are of different types. Let's understand it in detail.

Variable is of two types.

  1. Numerical or Quantitative Variables: These are the variables that can be measured or counted. Numbers, also known as quantifiers come under this category, for example, 1,2,4,6, etc. It is further classified into two types.

  • Continuous Variables: This variable takes any value between a certain set of real numbers. It can be infinite.

  • Discrete variables: This variable can take only whole numbers ie integer values. It is a countable quantity.

2. Categorical or Qualitative Variables: These are the variable that expresses a feeling or characteristics. Hence it can't be countable and hence can not be measured.

It is of two types.

  • Ordinal Variables: These are the variable arranged in a logical order. For example, Poor<Average<Good .

  • Nominal Variables: The variables which can't be ranked based on their values. For example, Colors.

Along with that, there are some more variables present, which are like:

  1. Independent Variables: They are the ones whose value doesn't depend on any other variables.

  2. Dependent Variables: They are the ones whose value depends on other independent variables and cannot be changed easily.

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