Dimensionality Reduction

Dimensionality Reduction techniques simplify datasets by reducing the number of input variables while preserving essential information. Mathematical methods transform high-dimensional data into a lower-dimensional space while maintaining essential patterns and relationships. Methods like Principal Component Analysis (PCA) help in visualizing data, improving model performance, and managing computational complexity while reducing noise.

Data visualization (plotting high-dimensional data) noise reduction feature compression speeding up algorithms preprocessing for machine learning tasks.