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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

Bivariate Analysis


In bivariate analysis, we extend our analysis to study two variables together. In our use case, we have around 20 independent variables. It is indeed possible to study all permutation combinations of the available 20 variables, but we won't go to that extent in this chapter. In our use case, we are more interested in studying all the factors that led to the poor performance of the campaign. Therefore, our primary focus will be to perform bivariate analysis and study the relationship between all the independent variables and our dependent target variable. Again, depending on the type of variable, we will have a different type of visual or analytical technique to analyze the relationship between the two variables. The possible combinations are numeric and numeric, and numeric and categorical. Given that our dependent variable is a categorical variable, we might have to explore the relationship between two independent variables in our list to study the relationship between...

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