Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm learns from unlabelled data — there are no predefined correct answers. The goal is to discover hidden patterns, structures, or groupings in the data without human guidance on what the output should be.

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How Unsupervised Learning Differs from Supervised Learning

Aspect	Supervised Learning	Unsupervised Learning
Data	Labelled (X, y)	Unlabelled (X only)
Goal	Predict known outputs	Discover hidden structure
Evaluation	Compare predictions to known labels	Domain knowledge, visual inspection, internal metrics
Examples	Spam detection, price prediction	Customer segmentation, anomaly detection

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Clustering

Clustering groups similar data points together based on their features. Points within the same cluster are more similar to each other than to points in other clusters.

K-Means Clustering

K-Means is the most popular clustering algorithm. It partitions data into k clusters by iteratively assigning points to the nearest cluster centre and updating the centres.

How K-Means works: