Data Types in Machine Learning: Numerical vs. categorical vs. Ordinal

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In this video, we will cover the difference between numerical, categorical and ordinal types of data.

1. Numerical Data:
- Numerical data also known as quantitative data represents a measurement or count.
- Examples: weight, blood pressure, and dollars count.
- Numerical data consists of two types as follows: (1) discrete and (2) continuous.

2. Categorical Data:
- Categorical data represents data that could be divided into groups.
- Examples: race, sex, age group, and educational level.

3. Ordinal Data:
- Ordinal data represents a mix between numerical and categorical data.
- Example: course ratings on Udemy!
- Data consists of categories such as numbers between 1 and 5, in which:
1 star means poor quality course
5 star means great quality course
- The numbers in each category have mathematical meaning.
- This what differentiates ordinal data from categorical data.
- For example, if you take the average of the 1000 reviews on Udemy per course, you will end up with an answer that have a meaning.
- This does not work if you have categorical data, you cannot average single and married and get meaningful results.

#DataTypes #MachineLearning #Numerical #Categorical #Ordinal
  1. Prof. Ryan Ahmed
  2. Data Types
  3. Machine Learning
  4. ordinal data
  5. categorical data
  6. numerical data
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