developer-roadmap/roadmaps/machine-learning/content/principal-component-analysis@K-x_L3z8JTSHwtTeHm4EG.md

Principal Component Analysis

Principal Component Analysis (PCA) is a technique used to reduce the number of variables in a dataset while preserving the most important information. It transforms the original variables into a new set of variables called principal components, which are ordered by the amount of variance they explain. The first principal component captures the most variance, the second captures the second most, and so on. By selecting a smaller number of these principal components, you can reduce the dimensionality of the data without losing too much information.

Visit the following resources to learn more:

• @article@PCA | scikit-learn
• @article@What is principal component analysis (PCA)?
• @video@PCA Analysis in Python Explained (Scikit - Learn)