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The Data Science Workshop

You're reading from   The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800566927
Length 824 pages
Edition 2nd Edition
Languages
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Authors (5):
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Robert Thas John Robert Thas John
Author Profile Icon Robert Thas John
Robert Thas John
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
Andrew Worsley Andrew Worsley
Author Profile Icon Andrew Worsley
Andrew Worsley
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Toc

Table of Contents (16) Chapters Close

Preface
1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning

Introduction

The previous chapter was all about improving our machine learning model, and interpreting its results and parameters to provide meaningful insights to the business. This chapter opens the third part of this book: enhancing your dataset. In the next three chapters, we are taking a step back and will be focusing on the key input of any machine learning model: the dataset. We will learn how to explore a new dataset, prepare it for the modeling stage, and create new variables (also called feature engineering). These are very exciting and important topics to learn about, so let's jump in.

When we mention data science, most people think about building fancy machine learning algorithms for predicting future outcomes. They usually do not think about all the other critical tasks involved in a data science project. In reality, the modeling step covers only a small part of such a project. You may have already heard about the rule of thumb stating that data scientists spend...

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