A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business…

Logistic regression is a classification algorithm used for predicting discrete categories or probabilities (e.g., fraudulent/not fraudulent), not continuous numerical values.

Linear regression is a supervised learning algorithm specifically designed for predicting a continuous target variable, such as housing prices, based on one or more independent features. It finds a linear relationship between the input features and the output value, making it suitable for this business requirement.

K-means is an unsupervised clustering algorithm used to group similar data points together, not for predicting a target variable.

Principal Component Analysis (PCA) is a dimensionality reduction technique, not a predictive model used for forecasting prices directly.