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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Summary


In this chapter, we visited some basics of R programming data types, data structures, and important functions and packages. We described vectors, matrix, list, DataFrame, and data tables as different forms of storing data. In the data processing and transformation space, we explore the cbind, rbind, merge, reshape, aggregate, and apply family of functions.

We also discussed the most important packages in R such as dplyr, tidyr, and plyr. In the end, the ggplot2 data visualization package was used to demonstrate various types of visualization and how to draw insights from them.

In the next chapter, we will use all that you have learned in this chapter for performing Exploratory Data Analysis (EDA). In EDA, data transformation and visualization you learned here will be useful for drawing inferences from data.

You have been reading a chapter from
Applied Supervised Learning with R
Published in: May 2019
Publisher:
ISBN-13: 9781838556334
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