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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
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Anthony So
Dr. Samuel Asare Dr. Samuel Asare
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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

Boxplots

Now, we will have a look at another specific type of chart called a boxplot. This kind of graph is used to display the distribution of a variable based on its quartiles. Quartiles are the values that split a dataset into quarters. Each quarter contains exactly 25% of the observations. For example, in the following sample data, the quartiles will be as follows:

Figure 10.35: Example of quartiles for the given data

So, the first quartile (usually referred to as Q1) is 4; the second one (Q2), which is also the median, is 5; and the third quartile (Q3) is 8.

A boxplot will show these quartiles but also additional information, such as the following:

  • The interquartile range (or IQR), which corresponds to Q3 - Q1
  • The lowest value, which corresponds to Q1 - (1.5 * IQR)
  • The highest value, which corresponds to Q3 + (1.5 * IQR)
  • Outliers, that is, any point outside of the lowest and highest points:

Figure 10.36: Example of a boxplot...

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