Answer: K-means

## Explanation K-means clustering is the appropriate machine learning algorithm for this scenario because: - **Clustering Purpose**: The bank wants to "separate its savers into groups" based on customer characteristics (age, marriage status, salary), which is a classic clustering problem - **Unsupervised Learning**: K-means is an unsupervised learning algorithm that groups similar data points together without predefined labels - **Feature-Based Grouping**: The algorithm can handle multiple numerical features (age, salary) and categorical features (marriage status) to create meaningful customer segments **Why other options are incorrect**: - **Natural language processing (A)**: Used for text analysis, not customer segmentation - **Logistic regression (B)**: A classification algorithm for predicting binary outcomes, not grouping similar customers - **Principal components analysis (C)**: A dimensionality reduction technique, not a clustering algorithm K-means would help the bank identify natural customer segments for targeted marketing, product offerings, or risk assessment.

Author: LeetQuiz .