Solution

A decision tree is a popular algorithm for predictive modeling when the target variable is categorical. It splits the dataset into branches based on feature values, leading to predictions that classify the data into categories. Decision trees are simple to understand, interpret, and can handle both categorical and continuous input features. Why Other Options are Wrong: a) Linear regression is used for continuous target variables, not categorical ones. b) K-means clustering is an unsupervised learning algorithm for grouping data, not for classification. d) PCA is a dimensionality reduction technique, not a predictive modeling algorithm. e) SVD is a matrix factorization technique used in data reduction, not in classification tasks.