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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

Getting Started with the Use Case


In this chapter, we will refer to the rainfall prediction problem using the weather dataset, obtained from the Australian Commonwealth Bureau of Meteorology and made available through R. The dataset has two target variables, RainTomorrow, a flag indicating whether it will rain tomorrow, and RISK_MM, which measures the amount of rainfall for the following day.

In a nutshell, we can use this dataset for regression as well as classification, since we have two target variables. However, we will drop the continuous target variable and only consider the categorical target variable, RainTomorrow, for our classification exercise. The metadata and additional details about the dataset are available to explore at https://www.rdocumentation.org/packages/rattle/versions/5.2.0/topics/weather. Since the dataset is readily available through R, we don't need to separately download it; instead, we can directly use the R function within the rattle library to load the data into...

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