Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of datasets while retaining the most important information. It does this by transforming correlated variables into a smaller set of uncorrelated variables called principal components.
K-Means clustering is one of the simplest and most effective unsupervised machine learning algorithms. It is widely used for tasks such as customer segmentation and anomaly detection
Decision trees are powerful models that simulate human decision-making by breaking down complex problems into clear, step-by-step choices. They are widely used for classification and regression tasks, providing intuitive and interpretable results.