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The Applied Artificial Intelligence Workshop

You're reading from   The Applied Artificial Intelligence Workshop Start working with AI today, to build games, design decision trees, and train your own machine learning models

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800205819
Length 420 pages
Edition 1st Edition
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Authors (3):
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Anthony So Anthony So
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Anthony So
Zsolt Nagy Zsolt Nagy
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Zsolt Nagy
William So William So
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William So
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Toc

Table of Contents (8) Chapters Close

Preface
1. Introduction to Artificial Intelligence 2. An Introduction to Regression FREE CHAPTER 3. An Introduction to Classification 4. An Introduction to Decision Trees 5. Artificial Intelligence: Clustering 6. Neural Networks and Deep Learning Appendix

Data Preprocessing

Before building a classifier, we need to format our data so that we can keep relevant data in the most suitable format for classification and remove all the data that we are not interested in.

The following points are the best ways to achieve this:

  • Replacing or dropping values:

    For instance, if there are N/A (or NA) values in the dataset, we may be better off substituting these values with a numeric value we can handle. Recall from the previous chapter that NA stands for Not Available and that it represents a missing value. We may choose to ignore rows with NA values or replace them with an outlier value.

    Note

    An outlier value is a value such as -1,000,000 that clearly stands out from regular values in the dataset.

    The fillna() method of a DataFrame does this type of replacement. The replacement of NA values with an outlier looks as follows:

    df.fillna(-1000000, inplace=True)

    The fillna() method changes all NA values into numeric values.

    This numeric value...

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