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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
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Robert Thas John
Thomas Joseph Thomas Joseph
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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
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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

In previous chapters, where an introduction to machine learning was covered, you were introduced to two broad categories of machine learning; supervised learning and unsupervised learning. Supervised learning can be further divided into two types of problem cases, regression and classification. In the last chapter, we covered regression problems. In this chapter, we will peek into the world of classification problems.

Take a look at the following Figure 3.1:

Figure 3.1: Overview of machine learning algorithms

Classification problems are the most prevalent use cases you will encounter in the real world. Unlike regression problems, where a real numbered value is predicted, classification problems deal with associating an example to a category. Classification use cases will take forms such as the following:

  • Predicting whether a customer will buy the recommended product
  • Identifying whether a credit transaction is fraudulent
  • ...
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