Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)

Determining the number of local maxima and minima

Time series can be regarded as a signal, such as sound or electrocardiograms, and thus we can extract features that capture some of the complexity of the signal. Examples of signal complexity include the maximum or mean values, as we discussed in the previous recipes. We can also extract more complex features such as the number of local maxima or minima, or even more complex ones, such as the coefficients of the courier transform.

In this recipe, we will determine the number of local maxima and minima manually using the signal module from SciPy in combination with pandas. Then, we will point you to a Python package that extracts these and other complex signal processing parameters automatically that you can explore and use to expand your toolset.

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