Explanation: K-means clustering is a powerful unsupervised learning algorithm for grouping customers into distinct segments based on similar purchasing behaviors. It minimizes intra-cluster variance and ensures that customers in the same cluster exhibit closely related characteristics, such as spending frequency, product preferences, or average order value. Retailers can use these clusters to personalize marketing campaigns, recommend products, and allocate resources effectively. For instance, a cluster with high-spending customers might be targeted with premium offers, while infrequent buyers might receive discounts. K-means is computationally efficient and provides actionable insights for customer segmentation. Option A: Regression analysis predicts outcomes but does not group customers into distinct segments. Option C: PCA reduces dimensionality and aids visualization but is not inherently a segmentation technique. Option D: Sentiment analysis evaluates customer opinions but is unrelated to purchasing behavior segmentation. Option E: Time series analysis identifies trends over time but does not classify customers into groups.
The plea claimed that the appoinment (A) was made in utter violation of the settled principles of law, which mandates (B) that important government ...
To speak or write about something in detail
Empathy
...A) Clang B) Impudence C) Peal D) Scorn
...Algophobia
Find out the compound word from the following options.
Mammoth
Select the most appropriate ANTONYM of the given word.
Timid
A) Diligent B) Lazy C) Despair D) Hatch
...In each of the following questions, three out of four words given have the same meaning. Mark the number as your answer which is different in meaning f...
