Unsupervised Learning

Unsupervised Learning deals with data that has no labeled responses. The model tries to learn the underlying structure or patterns in the data without explicit guidance. The goal is to discover hidden patterns, groupings, or relationships within the dataset.

Key tasks in unsupervised learning include:

Clustering: Grouping similar data points together (e.g., customer segmentation using K-means clustering).
Dimensionality Reduction: Reducing the number of features while preserving important information (e.g., using Principal Component Analysis - PCA).

Common algorithms used in unsupervised learning:

K-Means Clustering for partitioning data into clusters.
Hierarchical Clustering for creating nested groupings.
Principal Component Analysis (PCA) for dimensionality reduction.
Autoencoders for learning efficient data representations.
Unsupervised learning is often applied in market segmentation, anomaly detection, and recommendation systems.