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)