Dimensionality Reduction

Dimensionality reduction is an important task in data science that aims to reduce the dimensionality of a dataset through mathematical methods, thereby reducing redundant information, improving computational efficiency, and enhancing model performance. This process helps uncover the intrinsic structure of the data, improves data visualization, and plays a crucial role in feature selection and data compression.