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Published inJan 2022
PublisherPackt
ISBN-139781801072137
Edition1st Edition
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Roy Jafari
Roy Jafari
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Roy Jafari

Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
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Performing numerosity data reduction

When we need to reduce the number of data objects (rows) as opposed to the number of attributes (columns), we have a case of numerosity reduction. In this section, we will cover three methods: random sampling, stratified sampling, and random over/undersampling. Let's start with random sampling.

Random sampling

Randomly selecting some of the rows to be included in the analysis is known as random sampling. The reason we are compelled to accept random sampling is when we run into computational limitations. This normally happens when the size of our data is bigger than our computational capabilities. In those situations, we may randomly select a subset of the data objects to be included in the analysis. Let's look at an example.

Example – random sampling to speed up tuning

In this example, we are using Customer Churn.csv to train a decision tree so that it can predict (classify) what customer will be churning in the future...

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Hands-On Data Preprocessing in Python
Published in: Jan 2022Publisher: PacktISBN-13: 9781801072137

Author (1)

author image
Roy Jafari

Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands. Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization. Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book. Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.
Read more about Roy Jafari