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The Data Science Workshop

The Data Science Workshop

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

Handling Incorrect Values

Another issue you may face with a new dataset is incorrect values for some of the observations in the dataset. Sometimes, this is due to a syntax error; for instance, the name of a country may be written all in lower case, all in upper case, as a title (where only the first letter is capitalized), or may even be abbreviated. France may take different values, such as 'France', 'FRANCE', 'france', 'FR', and so on. If you define 'France' as the standard format, then all the other variants are considered incorrect values in the dataset and need to be fixed.

If this kind of issue is not handled before the modeling phase, it can lead to incorrect results. The model will think these different variants are completely different values and may pay less attention to these values since they have separated frequencies. For instance, let's say that 'France' represents 2% of the value, 'FRANCE&apos...

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