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K-means clustering is well-suited for customer segmentation as it groups customers based on similarities in purchasing behaviors, allowing retailers to identify clusters of customers with similar shopping patterns. By analyzing customer purchasing data, K-means assigns each customer to the nearest cluster center, effectively organizing the customer base into distinct segments. This method is ideal for developing targeted marketing strategies, as each segment represents a specific customer type that can be addressed with tailored promotions. K-means is computationally efficient and works well with large datasets, making it a popular choice for customer segmentation in retail. The other options are incorrect because: • Linear Regression is used for continuous prediction, not clustering. • Principal Component Analysis (PCA) reduces dimensions but doesn’t segment data into groups. • Logistic Regression is a classification tool, not suited for unsupervised segmentation. • Naive Bayes Classifier is a supervised technique that classifies data based on probability, not clustering.
Read the sentence to find out whether there is any grammatical or idiomatic error in it. The error, if any, will be in two parts of the sentence. Mark ...
Directions: In each of the questions, a sentence has been divided into four parts, one of which may contain an error. Identify that fragment and mark ...
Parts of the following sentence are given as options. Identify the segment that contains a grammatical error.
I am sorry I haven’t learnt the...
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