How can analyzing the sepal width of the Iris dataset enhance business intelligence in predicting customer preferences for flower types using machine learning techniques?

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Seekh · Accepted Answer

Analyzing sepal width helps reveal subtle patterns in flower shape that customers often notice, even if they don't realize it; this data can be fed into a machine‑learning model that learns which widths tend to be chosen by different customer groups. By training a classifier on sepal width alongside other features, the model can predict which Iris variety a new customer might prefer based on their past purchases or survey responses. The business can then use those predictions to stock the right mix of flowers or tailor marketing messages, improving customer satisfaction and sales. For example, if the model finds that customers who bought roses with a sepal width around 3. 5 cm also liked tulips with a similar width, the store can recommend tulips when a customer shows interest in roses of that size.

How can analyzing the sepal width of the Iris dataset enhance business intelligence in predicting customer preferences for flower types using machine learning techniques?

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