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The Data Science Workshop

The Data Science Workshop - Second Edition

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
3 (2)
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
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Preface
12
12. Feature Engineering

Summary

In this chapter, we learned about imbalanced datasets and strategies for addressing imbalanced datasets. We introduced the use cases where imbalanced datasets would be encountered. We looked at the challenges posed by imbalanced datasets and we were introduced to the metrics that should be used in the case of an imbalanced dataset. We formulated strategies for dealing with imbalanced datasets and implemented different strategies, such as random undersampling and oversampling, for balancing datasets. We then fit different models after balancing the datasets and analyzed the results.

Balancing datasets is a very effective way to improve the performance of your classifiers. However, it should be noted that there could be a degradation of overall accuracy measures for the majority class due to balancing. What strategies to adopt in what situations should be arrived at based on the problem statement and also after rigorous experiments for those problem statements.

Having learned...

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The Data Science Workshop
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