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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is K-Means clustering?
π‘ Hint: Think about the core idea of grouping based on distance.
Question 2
Easy
Explain the steps involved in the K-Means algorithm.
π‘ Hint: Consider the iterative nature of the algorithm.
Practice 1 more question and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does K-Means clustering primarily aim to achieve?
π‘ Hint: Remember what K-Means does with data.
Question 2
The Elbow Method helps determine what?
π‘ Hint: Think about cluster selection techniques.
Solve and get performance evaluation
Push your limits with challenges.
Question 1
You have a dataset with many outliers. Explain how K-Means will handle this and propose a solution.
π‘ Hint: Consider how the mean is affected by extreme values.
Question 2
Youβre tasked with clustering customer data. Describe how you would determine the optimal K and why it's important.
π‘ Hint: Think about accuracy in your clustering results.
Challenge and get performance evaluation