Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model is trained on data without labeled outcomes. Instead of being told what to predict, the algorithm identifies hidden patterns, groupings, or structures in the input data on its own.

It is commonly used for:

• Clustering (e.g., customer segmentation)
• Dimensionality reduction (e.g., data visualization, noise reduction)
• Anomaly detection (e.g., fraud detection)

Unsupervised learning helps uncover insights from data when explicit labels or outcomes are not available.