Dimension reduction is a technique used in data analysis and machine learning to simplify and compress data while retaining important information. It involves reducing the number of variables or features in a dataset by transforming them into a lower-dimensional space. This can help to eliminate noise, improve computational efficiency, and enhance the interpretability of the data. Dimension reduction methods include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA).