Principal Component Analysis (PCA)

Principal Component Analysis is an unsupervised statistical technique that transforms a dataset into a lower-dimensional space while retaining the most significant variance among the data points. By identifying the principal components, PCA reduces the complexity of the dataset, making it easier to visualize and analyze while minimizing information loss. This technique is widely used in data preprocessing and exploratory data analysis.