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R Data Mining

You're reading from   R Data Mining Implement data mining techniques through practical use cases and real-world datasets

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Length 442 pages
Edition 1st Edition
Languages
Tools
Concepts
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Author (1):
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Andrea Cirillo Andrea Cirillo
Author Profile Icon Andrea Cirillo
Andrea Cirillo
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Table of Contents (16) Chapters Close

Preface 1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER 2. A First Primer on Data Mining Analysing Your Bank Account Data 3. The Data Mining Process - CRISP-DM Methodology 4. Keeping the House Clean – The Data Mining Architecture 5. How to Address a Data Mining Problem – Data Cleaning and Validation 6. Looking into Your Data Eyes – Exploratory Data Analysis 7. Our First Guess – a Linear Regression 8. A Gentle Introduction to Model Performance Evaluation 9. Don't Give up – Power up Your Regression Including Multiple Variables 10. A Different Outlook to Problems with Classification Models 11. The Final Clash – Random Forests and Ensemble Learning 12. Looking for the Culprit – Text Data Mining with R 13. Sharing Your Stories with Your Stakeholders through R Markdown 14. Epilogue
15. Dealing with Dates, Relative Paths and Functions

A First Primer on Data Mining Analysing Your Bank Account Data

It should be now clear to you why R is worth investing your time in: it is a powerful language, plugin-ready, data visualization-friendly, and all the other adjectives you can derive from the previous chapter. Wouldn't it be great to taste a bit of all of those powerhouses?

That is what this chapter is all about—letting you experiment with discovering insights from your data with R. 

We are going to do this with your own data, in particular, your banking data. We are going to discover and model your expenditure habits, employing the power of R. After reading this chapter, apart from being even more enthusiastic about reading the remaining chapters, you will be able to do the following:

  • Summarize your data with functions provided by dplyr
  • Answer questions regarding your finance habits...
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