> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Principal Component Analysis (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.
