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Feature Engineering Made Easy

You're reading from   Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Length 316 pages
Edition 1st Edition
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Authors (2):
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Divya Susarla Divya Susarla
Author Profile Icon Divya Susarla
Divya Susarla
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Feature Engineering 2. Feature Understanding – What's in My Dataset? FREE CHAPTER 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 9. Other Books You May Enjoy

Examining our dataset

For demonstrative purposes, in this chapter, we will utilize a dataset that we have created, so that we can showcase a variety of data levels and types. Let's set up our DataFrame and dive into our data.

We will use pandas to create the DataFrame we will work with, as this is the primary data structure in pandas. The advantage of a pandas DataFrame is that there are several attributes and methods available for us to perform on our data. This allows us to logically manipulate the data to develop a thorough understanding of what we are working with, and how best to structure our machine learning models:

  1. First, let's import pandas:
# import pandas as pd
  1. Now, we can set up our DataFrame X. To do this, we will utilize the DataFrame method in pandas, which creates a tabular data structure (table with rows and columns). This method can take in a few...
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