Individual skill
pca-decomposition
Reduce dimensionality of multivariate data using PCA with varimax rotation. Use when you have many correlated variables and need to identify underlying factors or reduce collinearity.
tasks/lake-warming-attribution/environment/skills/pca-decomposition/SKILL.md
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# PCA Decomposition Guide
## Overview
Principal Component Analysis (PCA) reduces many correlated variables into fewer uncorrelated components. Varimax rotation makes components more interpretable by maximizing variance.
## When to Use PCA
- Many correlated predictor variables
- Need to identify underlying factor groups
- Reduce multicollinearity before regression
- Exploratory data analysis
## Basic PCA with Varimax Rotation