What are the 4 types of data that machine learning can use?

Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.

• Nominal data.
• Ordinal data.
• Discrete data.
• Continuous data.

There are generally four types of data that machine learning algorithms can use:

Numeric data: This type of data consists of numbers and can include continuous values (such as prices or temperatures) or discrete values (such as counts or rankings). Numeric data can be used as input or output in machine learning algorithms

Categorical data: This type of data consists of categories or labels, such as names, types, or categories. Categorical data can be used as input or output in machine learning algorithms, but it may need to be converted into a numerical form in order to be used by certain algorithms.

Time series data: This type of data consists of measurements taken at regular intervals over a period of time. Time series data is often used in machine learning algorithms for tasks such as forecasting or trend analysis.

Text data: This type of data consists of written or spoken words and can include things like emails, social media posts, or customer reviews. Text data is often used in machine learning algorithms for tasks such as natural language processing or sentiment analysis.

It’s important to note that these are general categories of data and that different machine learning algorithms may be suited to different types of data or may be able to handle multiple types of data.