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Machine Learning with R

You're reading from Machine Learning with R Expert techniques for predictive modeling

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781788295864

Length 458 pages

Edition 3rd Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Author (1):

Brett Lantz

View More author details

Table of Contents (16) Chapters

Preface

1. Introducing Machine Learning FREE CHAPTER

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

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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.

Tip

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...

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Authors (1)

Brett Lantz

Brett Lantz

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

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