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"A. Advantages of K-means Clustering include its simplicity, scalability, and versatility, as it can be applied to a wide range of data types. Disadvantages include its sensitivity to the initial placement of centroids and its limitations in handling complex, non-linear data. k-means is also sensitive to outliers."
<https://www.analyticsvidhya.com/blog/2021/04/k-means-clustering-simplified-in-python/?utm_source=reading_list&utm_medium=https://www.analyticsvidhya.com/blog/2020/10/5-popular-data-science-languages-career/>