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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

Introduction

When you assess the performance of a model, you look at certain measurements or values that tell you how well the model is performing under certain conditions, and that helps you make an informed decision about whether or not to make use of the model that you have trained in the real world. Some of the measurements you will encounter in this chapter are MAE, precision, recall, and R2 score.

You learned how to train a regression model in Chapter 2, Regression, and how to train classification models in Chapter 3, Binary Classification. Consider the task of predicting whether or not a customer is likely to purchase a term deposit, which you addressed in Chapter 3, Binary Classification. You have learned how to train a model to perform this sort of classification. You are now concerned with how useful this model might be. You might start by training one model, and then evaluating how often the predictions from that model are correct. You might then proceed to train more...

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