Unsupervised learning is a branch of machine learning where models work independently to uncover patterns or structures in data without any predefined labels or targets. Instead of learning from examples paired with the “correct” answers (as in supervised learning), the model explores the underlying structure in the data to identify similarities, differences, or other informative relationships. Through these methods, unsupervised learning helps uncover hidden structures, relationships, and patterns in data that would otherwise remain undiscovered without labeled examples. Some of the most common techniques in unsupervised learning include:

Dimension Reduction
This involves reducing the number of variables (features) in a dataset while retaining as much information as possible. Methods like Principal Component Analysis (PCA) and t-SNE are examples of dimension reduction. They help simplify complex datasets, speed up model training, and reveal hidden patterns that might be obscured by higher-dimensional data.

Density Estimation
Density estimation aims to determine the probability distribution or “shape” of the data. Techniques such as Kernel Density Estimation (KDE) help model the probability density function of a dataset, providing insights about where data points are concentrated and where they are sparse.

Market Basket Analysis
Often used in retail and e-commerce, market basket analysis involves mining transactional data to find associations between products or items frequently purchased together. This technique can guide promotional strategies, store layout optimizations, and targeted recommendations.

Clustering
Clustering groups data points into clusters such that items within a cluster are more similar to each other than they are to items in other clusters. Common clustering algorithms include k-means, DBSCAN, and hierarchical clustering. Clustering is widely used in customer segmentation, anomaly detection, and image grouping, among other applications.