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DA0-002 Data Analysis Practice Question

A data scientist is building a K-means clustering model for customer segmentation. After plotting the within-cluster sum of squares (WCSS) against the number of clusters (k), she observes that the WCSS decreases sharply until k=5 and then levels off. Which value of k should she choose based on the elbow method?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

k=5

The elbow method suggests selecting the number of clusters at the point where the WCSS starts to diminish less rapidly, forming an 'elbow'. Here, the elbow is at k=5, where adding more clusters yields diminishing returns.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • k=5

    Why this is correct

    Correct elbow point.

  • k=6

    Why it's wrong here

    After the elbow, gains are minimal.

  • k=4

    Why it's wrong here

    The elbow is at k=5.

  • k=3

    Why it's wrong here

    The elbow is at k=5, not 3.

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