Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning and statistics. It transforms high-dimensional data into a lower-dimensional form while retaining as much variance as possible. PCA is particularly useful for visualizing data, reducing noise, and improving the performance of machine learning algorithms.

• Unsupervised learning technique
• Used for dimensionality reduction
• reduced dimensions are called components