Dimensionality reduction is a set of techniques used in machine learning to reduce the number of random variables, or features, under consideration. The goal is to transform high-dimensional data into a lower-dimensional representation while preserving as much of the relevant information as possible. This process addresses the "curse of dimensionality," where the sparsity of data in high-dimensional spaces makes it difficult for machine learning algorithms to find meaningful patterns and leads to increased computational complexity and storage requirements. Techniques for dimensionality reduction can be broadly categorized into feature selection (selecting a subset of the original features) and feature extraction (transforming the original features into a new, smaller set of features). Popular methods include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and autoencoders. By simplifying data representation, dimensionality reduction can improve model performance, reduce training time, and facilitate data visualization and interpretation.