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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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 Toomey Toomey
Author Profile Icon Toomey
Toomey
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Toc

Table of Contents (19) Chapters Close

R for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Data Mining Patterns 2. Data Mining Sequences FREE CHAPTER 3. Text Mining 4. Data Analysis – Regression Analysis 5. Data Analysis – Correlation 6. Data Analysis – Clustering 7. Data Visualization – R Graphics 8. Data Visualization – Plotting 9. Data Visualization – 3D 10. Machine Learning in Action 11. Predicting Events with Machine Learning 12. Supervised and Unsupervised Learning Index

Questions


Factual

  • How do you decide whether to use kmeans or kdemoids?

  • What is the significance of the boxplot layout? Why does it look that way?

  • Describe the underlying data produced in the outliers for the iris data, given the density plot.

  • What are the extract rules for other items in the market dataset?

When, how, and why?

  • What is the risk of not vetting the outliers that are detected for the specific domain? Shouldn't the calculation always work?

  • Why do we need to exclude the iris category column from the outlier detection algorithm? Can it be used in some way when determining outliers?

  • Can you come up with a scenario where the market basket data and rules we generated were not applicable to the store you are working with?

Challenges

  • I found it difficult to develop test data for outliers in two dimensions that both occurred in the same instance using random data. Can you develop a test that would always have several outliers in at least two dimensions that occur in the same instance?

  • There is a good dataset on the Internet regarding passenger data on the Titanic. Generate the rules regarding the possible survival of the passengers.

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