While previous chapters dealt primarily with supervised learning tasks requiring labeled data, many datasets lack explicit target variables. This chapter focuses on unsupervised learning, a set of techniques used to discover inherent structures, patterns, and relationships within unlabeled data.

You will learn to implement practical methods for:

• Clustering: Grouping similar data points together using algorithms like K-Means and the density-based approach DBSCAN.
• Anomaly Detection: Identifying unusual or outlier data points that deviate significantly from the norm.
• Dimensionality Reduction for Visualization: Reducing the number of features using techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to facilitate the visualization of high-dimensional datasets.

Through practical examples and hands-on exercises, you will gain experience in applying these unsupervised methods to analyze and understand data without predefined labels.