Understanding Numerical and Categorical Data in Machine Learning

This tutorial discusses different types of data used in machine learning. This tutorial embarks on a journey through the definitions, classifications, and key characteristics of numerical and categorical data.

1. Numerical Data

Numerical data, also known as quantitative data, is a type of data that expresses quantities and is measured on a numerical scale. It represents measurable and countable values that can be subjected to mathematical operations, allowing for quantitative analysis. Numerical data can take various forms, and its classification depends on the nature of the values it represents.

1.1 Classification of Numerical Data

Numerical data can be of two types: Continuous Numerical Data and Discrete Numerical Data.

1.1.1 Continuous Numerical Data

Continuous numerical data consists of values that can take any real number within a given range. These values are often obtained through measurement and can have infinite possible values between any two points.
Examples: Temperature readings (e.g., 23.5°C), height measurements, weight, and time intervals.