Answer: Supervised Learning (Regression)

## Explanation This is a **supervised learning regression** problem because: 1. **Labeled Data**: We have a dataset with features (location, size, number of bedrooms) and corresponding **target values** (house prices). This is the key characteristic of supervised learning. 2. **Regression Task**: The target variable (house price) is a **continuous numerical value**. Regression models predict continuous values, while classification models predict discrete categories. 3. **Why not other options**: - **Unsupervised Learning (A)**: Used when there are no labels/target values. The algorithm finds patterns or structures in the data without guidance. - **Reinforcement Learning (B)**: Involves an agent learning through trial and error by interacting with an environment to maximize cumulative reward. - **Clustering (D)**: A type of unsupervised learning that groups similar data points together without predefined categories. 4. **Real-world application**: Predicting house prices based on features is a classic regression problem in machine learning, often solved using algorithms like linear regression, decision trees, or neural networks. **Correct Answer: C - Supervised Learning (Regression)**

Author: Jin H