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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

Studying the Relationship between Two Numeric Variables


To understand how we can study the relationship between two numeric variables, we can leverage scatter plots. It is a 2-dimensional visualization of the data, where each variable is plotted on an axis along its length. Relationships between the variables are easily identified by studying the trend across the visualization. Let's take a look at an example in the following exercise.

Exercise 30: Studying the Relationship between Employee Variance Rate and Number of Employees

Let's study the relationship between employee variance rate and the number of employees. Ideally, the number of employees should increase as the variation rate increases.

Perform the following steps to complete the exercise:

  1. First, import the ggplot2 package using the following command:

    library(ggplot2)
  2. Create a DataFrame object, df, and use the bank-additional-full.csv file using the following command:

    df <- read.csv("/Chapter 2/Data/bank-additional/bank-additional-full...
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