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Machine Learning with R - Third Edition

You're reading from  Machine Learning with R - Third Edition

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Pages 458 pages
Edition 3rd Edition
Languages
Author (1):
Brett Lantz Brett Lantz
Profile icon Brett Lantz

Table of Contents (18) Chapters

Machine Learning with R - Third Edition
Contributors
Preface
Other Books You May Enjoy
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1. Introducing Machine Learning 2. Managing and Understanding Data 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Index

Exploring and understanding data


After collecting data and loading it into R data structures, the next step in the machine learning process involves examining the data in detail. It is during this step that you will begin to explore the data's features and examples, and realize the peculiarities that make your data unique. The better you understand your data, the better you will be able to match a machine learning model to your learning problem.

The best way to learn the process of data exploration is by example. In this section, we will explore the usedcars.csv dataset, which contains actual data about used cars advertised for sale on a popular US website in the year 2012.

Note

The usedcars.csv dataset is available for download on the Packt Publishing support page for this book. If you are following along with the examples, be sure that this file has been downloaded and saved to your R working directory.

Since the dataset is stored in CSV form, we can use the read.csv() function to load the...

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