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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

Summary

In this chapter, we have investigated the use of autoregression models, which predict future values based on the temporal behavior of prior data in the series. Using autoregression modeling, we were able to accurately model the closing price of the S&P 500 over the years 1986 to 2018 and a year into the future. On the other hand, the performance of autoregression modeling to predict annually periodic temperature data for Austin, Texas, seemed more limited.

Now that we have experience with regression problems, we will turn our attention to classification problems in the next chapter.

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