Dimensionality Reduction Overview Dimensionality reduction techniques reduce the number of features while preserving important information, improving model efficiency and enabling visualization of high-dimensional data. When to Use - High-dimensional datasets with many features - Visualizing complex datasets in 2D or 3D - Reducing computational complexity and training time - Removing redundant or highly correlated features - Preventing overfitting in machine learning models - Preprocessing data before clustering or classification Techniques - PCA : Principal Component Analysis - t-SNE : t-Dis…