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Data Wrangling with R

You're reading from   Data Wrangling with R Load, explore, transform and visualize data for modeling with tidyverse libraries

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803235400
Length 384 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Gustavo Santos Gustavo Santos
Author Profile Icon Gustavo Santos
Gustavo Santos
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Load and Explore Data
2. Chapter 1: Fundamentals of Data Wrangling FREE CHAPTER 3. Chapter 2: Loading and Exploring Datasets 4. Chapter 3: Basic Data Visualization 5. Part 2: Data Wrangling
6. Chapter 4: Working with Strings 7. Chapter 5: Working with Numbers 8. Chapter 6: Working with Date and Time Objects 9. Chapter 7: Transformations with Base R 10. Chapter 8: Transformations with Tidyverse Libraries 11. Chapter 9: Exploratory Data Analysis 12. Part 3: Data Visualization
13. Chapter 10: Introduction to ggplot2 14. Chapter 11: Enhanced Visualizations with ggplot2 15. Chapter 12: Other Data Visualization Options 16. Part 4: Modeling
17. Chapter 13: Building a Model with R 18. Chapter 14: Build an Application with Shiny in R 19. Conclusion 20. Other Books You May Enjoy

Summary

In this chapter, we created an end-to-end machine learning project. We started by studying some basic machine learning concepts to put us in sync. Then, we understood what was needed for the main goal of the project. First, we must understand the problem and know where we want to go so that the solution becomes clearer. In this case, our client was a digital marketing company that wanted to reduce the risk of their messages ending up in their spam filter, so we had to create a classification model to predict the probability of a message being marked as spam or not spam.

We loaded a dataset from UCI, which brought up some words and characters associated with spam messages and their percentage in the email. Then, we studied the data and created some visualizations to learn which elements were more likely to be classified as spam. Out of those, we created a new dataset with just six explanatory variables, reducing it from the original 57 columns.

Next, we trained and tested...

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