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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

In the previous chapter, Chapter 12, Feature Engineering, where we dealt with data points related to dates, we were addressing scenarios pertaining to features. In this chapter, we will deal with scenarios where the proportions of examples in the overall dataset pose challenges.

Let's revisit the dataset we dealt with in Chapter 3, Binary Classification, in which the examples pertaining to 'No' for term deposits far outnumbered the ones with 'Yes' with a ratio of 88% to 12%. We also determined that one reason for suboptimal results with a logistic regression model on that dataset was the skewed proportion of examples. Datasets like the one we analyzed in Chapter 3, Binary Classification, which are called imbalanced datasets, are very common in real-world use cases.

Some of the use cases where we encounter imbalanced datasets include the following:

  • Fraud detection for credit cards or insurance claims
  • Medical diagnoses where we...
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