A real estate firm wants to predict house prices using a historical dataset with 32 features. Which modeling approach is appropriate for this task?

Linear regression is appropriate because house price prediction is a classic regression problem where the goal is to predict a continuous numerical value (house price). Linear regression models the relationship between the independent variables (features like number of bedrooms, square footage, etc.) and the dependent variable (house price) as a linear equation. Logistic regression is used for binary or multi-class classification problems, not for predicting continuous values. K-means clustering is an unsupervised learning algorithm used for grouping similar data points, not for prediction. Principal Component Analysis (PCA) is a dimensionality reduction technique used for feature engineering, not for building a predictive model itself.