A retail analyst is preparing a customer-level table for a marketing dashboard. The numeric field lifetime_value ranges from 4 USD to 18 960 USD and is heavily skewed. The marketing team wants the dashboard to display five segments (very-low, low, medium, high, very-high) so that roughly 20 % of customers fall into each segment. Which data-transformation technique will most efficiently create the required five equally populated groups?
Equal-frequency (also called quantile) binning sorts the values and cuts them at the 20th, 40th, 60th and 80th percentiles, guaranteeing that each of the five bins contains approximately the same number of customers. k-means clustering can produce five clusters, but cluster sizes vary because the algorithm minimizes within-cluster variance, not record counts. Min-max scaling only rescales every value to a 0-1 interval and does not create categories. One-hot encoding turns each distinct category into binary indicator columns and is meant for categorical rather than continuous data.
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CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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