Answer: Supervised Learning

## Explanation **Supervised Learning** is the correct approach because: - The provider has **labeled training data** - millions of emails already tagged as "spam" or "not spam" - This is a **classification problem** where we need to predict categorical labels (spam vs not spam) - Supervised learning algorithms learn from labeled examples to make predictions on new, unseen data **Why other options are incorrect:** - **A) Unsupervised Learning**: Used when there are no labels, for finding patterns or groupings in data - **B) Reinforcement Learning**: Used for decision-making problems where an agent learns through trial and error with rewards/penalties - **D) Clustering**: A type of unsupervised learning for grouping similar data points without predefined labels **Common supervised learning algorithms** for this type of classification problem include: - Logistic Regression - Support Vector Machines (SVM) - Random Forests - Neural Networks The labeled dataset allows the model to learn the patterns and characteristics that distinguish spam emails from legitimate ones.

Author: Ritesh Yadav