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Getting Started with Haskell Data Analysis

You're reading from   Getting Started with Haskell Data Analysis Put your data analysis techniques to work and generate publication-ready visualizations

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789802863
Length 160 pages
Edition 1st Edition
Languages
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Author (1):
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James Church James Church
Author Profile Icon James Church
James Church
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Toc

Application of the KDE

This section will serve as our real-world application of the kernel density estimator. In this section, we're going to take a look at the Monet dataset. Monet was a famous French impressionist painter, and many of his paintings have sold at auction for millions of dollars. The Monet dataset is a record of all of those final auction prices for his paintings. We'll be discussing the parts of the kernel density estimator function, and then we will be answering the following question, using the kernel density estimator: what is the probability that, in the future, a Monet painting will sell for 5 million dollars or more? Let's do a Google search for Monet paintings, as illustrated by the following screenshot:

This is my excuse for putting beautiful Monet paintings in our book, and we're going to be discussing the auction prices of these...

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