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Feature Engineering Made Easy, published by Packt

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Feature Engineering Made Easy

This is the code repository for Feature Engineering Made Easy, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.

You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.

By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following: ``` Networkfeatures = pd.DataFrame({'datetime': ['6/2/2018', '6/2/2018', '6/2/2018', '6/3/2018'], 'protocol': ['tcp', 'http', 'http', 'http'], 'urgent': [False, True, True, False]}) Networkresponse = pd.Series([True, True, False, True]) Network_features

datetime protocol urgent 0 6/2/2018 tcp False 1 6/2/2018 http True 2 6/2/2018 http True 3 6/3/2018 http False Network_response

0 True 1 True 2 False 3 True dtype: bool ```

This book uses Python to complete all of its code examples. A machine (Linux/Mac/Windows is OK) with access to a Unix-style terminal and Python 2.7 installed is required. Installing the Anaconda distribution is also recommended as it comes with most of the packages used in the examples.

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