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Applied Supervised Learning with Python

You're reading from   Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781789954920
Length 404 pages
Edition 1st Edition
Languages
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Authors (2):
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Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
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Benjamin Johnston
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Toc

Missing Values


When there is no value (that is, a null value) recorded for a particular feature in a data point, we say the data is missing. Having missing values in a real dataset is inevitable; no dataset is ever perfect. However, it is important to understand why the data is missing, and if there is a factor that has affected the loss of data. Appreciating and recognizing this allows us to handle the remaining data in an appropriate manner. For example, if the data is missing randomly, then it's highly likely that the remaining data is still representative of the population. However, if the missing data is not random in nature and we assume that it is, it could bias our analysis and subsequent modeling.

Let's look at the common reasons (or mechanisms) for missing data:

  • Missing Completely at Random (MCAR): Values in a dataset are said to be MCAR if there is no correlation whatsoever between the value missing and any other recorded variable or external parameter. This means that the remaining...

You have been reading a chapter from
Applied Supervised Learning with Python
Published in: Apr 2019
Publisher:
ISBN-13: 9781789954920
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