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The Data Science Workshop

You're reading from   The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800566927
Length 824 pages
Edition 2nd Edition
Languages
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Authors (5):
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Robert Thas John Robert Thas John
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Robert Thas John
Thomas Joseph Thomas Joseph
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Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
Andrew Worsley Andrew Worsley
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Andrew Worsley
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Toc

Table of Contents (16) Chapters Close

Preface
1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning

Feature Engineering

In the previous section, we traversed the process of EDA. As part of the earlier process, we tested our business hypotheses by slicing and dicing the data and through visualizations. You might be wondering where we will use the intuitions that we derived from all of the analysis we did. The answer to that question will be addressed in this section.

Feature engineering is the process of transforming raw variables to create new variables and this will be covered later in the chapter. Feature engineering is one of the most important steps that influence the accuracy of the models that we build.

There are two broad types of feature engineering:

  1. Here, we transform raw variables based on intuitions from a business perspective. These intuitions are what we build during the exploratory analysis.
  2. The transformation of raw variables is done from a statistical and data normalization perspective.

We will look into each type of feature engineering...

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