one-hot encoding pca

One-hot encoding is a technique used to represent categorical variables as a binary vector. Each category is represented by a unique binary vector where only one element is set to 1 and the rest are set to 0. This technique is commonly used to preprocess categorical data for machine learning algorithms.

PCA (Principal Component Analysis) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much of the original variability as possible. It works by identifying the principal components (linear combinations of the original variables) that explain the most variance in the data. PCA is commonly used in data analysis, data compression, and machine learning.