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Machine Learning with R Cookbook, Second Edition - Second Edition

You're reading from  Machine Learning with R Cookbook, Second Edition - Second Edition

Product type Book
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Pages 572 pages
Edition 2nd Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)
Toc

Table of Contents (21) Chapters close

Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Practical Machine Learning with R 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Imputing missing values


After detecting the number of missing values within each attribute, we have to impute them since they might have a significant effect on the conclusions that can be drawn from the data.

Getting ready

This recipe will require mydata to be loaded in the R session and have the previous recipe completed.

How to do it...

Perform the following steps to impute the missing values:

  1. List the Ozone attribute:
        > mydata$Ozone
        Output
       [1] 41 36 12 18 NA 28 23 19 8 NA 7 16 11 14 18 14 34 6 30 11
      [21] 1 11 4 32 NA NA NA 23 45 115 37 NA NA NA NA NA NA 29 NA 71
      [41] 39 NA NA 23 NA NA 21 37 20 12 13 NA NA NA NA NA NA NA NA NA
      [61] NA 135 49 32 NA 64 40 77 97 97 85 NA 10 27 NA 7 48 35 61 79
      [81] 63 16 NA NA 80 108 20 52 82 50 64 59 39 9 16 78 35 66 122 89
      [101] 110 NA NA 44 28 65 NA 22 59 23 31 44 21 9 NA 45 168 73 NA 76
      [121] 118 84 85 96 78 73 91 47 32 20 23 21 24 44 21 28 9 13 46 18
      [141] 13 24 16 13 23 36 7 14 30 NA 14...
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