Meaning
A type of machine learning that analyzes data without labeled responses.
Definition
Unsupervised learning identifies patterns and structures in data without pre-existing labels.
The model explores the data and attempts to find hidden patterns or groupings within it, making it ideal for exploratory data analysis.
This approach is useful when the goal is to discover underlying structures within the data, such as clusters or associations, without needing explicit guidance.
Example
Market segmentation analysis uses unsupervised learning to group customers based on purchasing behavior.
By analyzing transaction data, the model can identify distinct segments within a customer base, allowing businesses to tailor marketing strategies to different customer groups.
