dimensionality-reduction

Dimensionality reduction techniques aim to simplify datasets by reducing the number of variables under consideration, which can help improve model performance and reduce computational costs. Common methods include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). By focusing on the most significant features, dimensionality reduction can enhance visualization and interpretation of high-dimensional data.