Principal Component Analysis for Dimensionality Reduction
Explore how Principal Component Analysis transforms high-dimensional data into fewer features while preserving essential information. Understand the steps of standardization, covariance matrix calculation, and eigen decomposition. Learn to select key components for simplifying data and enhancing model efficiency. Practice implementation using Scikit-Learn on the Iris dataset.
We'll cover the following...
We'll cover the following...
Principal Component Analysis
PCA stands for Principal Component Analysis. It helps us transform high-dimensional datasets (having a large number of features) into a low-dimensional one (having a smaller number of features) without losing too much information. These datasets can include images or simple structured datasets. This ...