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Hands-On Data Science with R

You're reading from  Hands-On Data Science with R

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Pages 420 pages
Edition 1st Edition
Languages
Authors (4):
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Profile icon Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Profile icon Doug Ortiz
Nataraj Dasgupta Nataraj Dasgupta
Profile icon Nataraj Dasgupta
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Profile icon Ricardo Anjoleto Farias
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with Data Science and R 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Summary

In this chapter, you learned what the terms KDD and data mining could mean. You have also learned about diverse ways of retrieving text from the web and even how to get a dwarf name for yourself. Otherwise, you may have learned how to run a term frequency and a clustering analysis. To wrap it up, here are the things that we did with Twitter data:

  • Cleaned and transformed data
  • Ran a term frequency analysis
  • Drew lollipop and word cloud charts to aid interpreting
  • Made hierarchical clustering from the term frequency

There is much more we could do with data retrieved from Twitter, such as the following:

  • Topic modeling
  • Sentiment analysis
  • Follower analysis
  • Retweet analysis—this might be useful for you to get more retweets
  • Favorite analysis

Given that we visited some ways of retrieving and manipulating data from Twitter, I am pretty confident that you can do this by...

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