Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," regression is a supervised machine learning technique used to predict continuous numeric values based on input data.
In this scenario, the goal is to predict how many hours of overtime a delivery person will work depending on the number of orders received. The output - the number of overtime hours - is a continuous variable (for example, 1.5 hours, 3.2 hours, etc.), not a category. This makes it a regression problem, where the model learns patterns from historical data and uses those patterns to estimate a continuous numeric outcome.
Why Regression Applies Here:
Regression models work by finding the mathematical relationship between input features (independent variables) and output values (dependent variables). In this case:
* Input (feature): Number of orders received
* Output (label): Predicted overtime hours
Azure Machine Learning supports several regression algorithms, including Linear Regression, Decision Tree Regression, and Neural Network Regression, all of which can handle scenarios where a numeric prediction is required.
Why Not the Other Options:
* Classification: Used for predicting discrete categories or labels (e.g., "on-time" vs. "late"). It does not output continuous numbers.
* Clustering: An unsupervised learning technique used to group data points with similar characteristics, not to make numeric predictions.
Thus, when the output variable is a numeric prediction (such as hours, prices, quantities, or time), the correct machine learning task is Regression.