PCA

Principal Component Analysis (PCA) reduces the number of features in your data while preserving the most important patterns. It transforms your original features into a smaller set of new features (principal components) that capture the maximum variance.

PCA is useful for visualization, noise reduction, and speeding up model training.

PCA works best when features are scaled always apply the Feature Scaler before PCA.

• Dimensionality reduction: Reduce many geometric features into 3 principal components for faster model training.
• Data visualization: Project high-dimensional data into 2D for visual exploration and cluster discovery.
• Noise reduction: Remove noisy, less important feature variations while keeping the dominant patterns in the data.