Answer: Supervised Learning (Regression)

## Explanation This is a **supervised learning regression** problem because: 1. **Labeled Data**: You have a dataset with features (location, size, number of bedrooms) and corresponding house prices (labels/target variable). 2. **Prediction Task**: The goal is to predict a continuous numerical value (house price) based on input features. 3. **Regression vs Classification**: Since the target variable (price) is continuous, this is a regression problem, not classification. **Why other options are incorrect:** - **A) Unsupervised Learning**: Used for finding patterns in unlabeled data (e.g., clustering, dimensionality reduction). Here we have labeled data. - **B) Reinforcement Learning**: Used for decision-making problems where an agent learns through rewards/penalties in an environment (e.g., game playing, robotics). - **D) Clustering**: A specific type of unsupervised learning for grouping similar data points, not for predicting continuous values. **Key Machine Learning Concepts:** - **Supervised Learning**: Training on labeled data to make predictions - **Regression**: Predicting continuous numerical values - **Classification**: Predicting discrete categories/classes In AWS context, this could be implemented using services like Amazon SageMaker for building, training, and deploying machine learning models.

Author: Ritesh Yadav