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Python Data Science Essentials

You're reading from   Python Data Science Essentials A practitioner's guide covering essential data science principles, tools, and techniques

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789537864
Length 472 pages
Edition 3rd Edition
Languages
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
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Toc

Table of Contents (11) Chapters Close

Preface 1. First Steps 2. Data Munging FREE CHAPTER 3. The Data Pipeline 4. Machine Learning 5. Visualization, Insights, and Results 6. Social Network Analysis 7. Deep Learning Beyond the Basics 8. Spark for Big Data 9. Strengthen Your Python Foundations 10. Other Books You May Enjoy

Building new features

Sometimes, you'll find yourself in a situation where features and target variables are not really related. In this case, you can modify the input dataset. You can apply linear or nonlinear transformations that can improve the accuracy of the system, and so on. It's a very important step for the overall process because it completely depends on the skills of the data scientist, who is the one responsible for artificially changing the dataset and shaping the input data for a better fit for the learning model. Although this step intuitively just adds complexity, this approach often boosts the performance of the learner; that's why it is used by bleeding-edge techniques, such as deep learning.

For example, if you're trying to predict the value of a house and you know the height, width, and the length of each room, you can artificially build...

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