Dimensionality Reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. This technique is used to simplify models, reduce computational costs, and overcome the curse of dimensionality in high-dimensional datasets. Popular methods include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), which transform the original data into a lower-dimensional space while preserving its essential structure and variance. Dimensionality reduction is crucial for data visualization, noise reduction, and improving the performance of machine learning models.