Dimensionality Reduction

Dimensionality reduction is a process used in data analysis and machine learning to simplify complex datasets by reducing the number of features or dimensions while preserving the most important information. By decreasing the dimensionality of a dataset, dimensionality reduction techniques help improve model performance, reduce noise and irrelevant data, and enable faster processing and visualization, making it a crucial step in data preprocessing and a key technique in the tech community for handling high-dimensional data.