Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 138 discussion

K-means is a clustering algorithm widely used for customer segmentation. It groups customers based on similarities in their demographics and buying patterns, creating distinct clusters that can be analyzed for targeted marketing strategies or personalized product offerings. This algorithm is efficient, interpretable, and works well with large datasets, making it suitable for e-commerce applications.

A. K-nearest neighbors (k-NN) - classification B. K-means - clustering - groups, so B

Let's break down why: Why K-means is correct: The company wants to find "groups" of customers → This indicates a clustering task K-means is specifically designed for grouping/clustering similar data points It works well with multiple features (demographics AND buying patterns) K-means can automatically discover natural groupings in customer data It's commonly used for customer segmentation in business applications Why other options are incorrect: A (K-nearest neighbors): This is for classification when you already have labeled data, not for discovering groups

K-means is a clustering algorithm that groups data points into clusters based on their similarities. It is particularly well-suited for unsupervised learning tasks where the goal is to identify natural groupings within the data, such as segmenting customers based on demographics and buying patterns.

Answer: B. K-means The company should use K-means to group customers based on demographics and buying patterns. K-means is an unsupervised clustering algorithm that effectively partitions data into natural groups, making it ideal for discovering customer segments without prior labeling.

K-means is a clustering algorithm