UMAP, or Uniform Manifold Approximation and Projection, is a dimensionality reduction technique used in data science and machine learning. It helps visualize high-dimensional data by reducing it to two or three dimensions while preserving the structure and relationships within the data. This makes it easier to analyze complex datasets and identify patterns. Developed by Leland McInnes, John Healy, and James Melville in 2018, UMAP is based on concepts from topology and manifold learning. It is particularly effective for tasks like clustering and classification, making it a popular choice for applications in fields such as bioinformatics, image processing, and natural language processing.