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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Master machine learning techniques with R to deliver insights for complex projects

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783984527
Length 400 pages
Edition 1st Edition
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Toc

Table of Contents (15) Chapters Close

Preface 1. A Process for Success FREE CHAPTER 2. Linear Regression – The Blocking and Tackling of Machine Learning 3. Logistic Regression and Discriminant Analysis 4. Advanced Feature Selection in Linear Models 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines 6. Classification and Regression Trees 7. Neural Networks 8. Cluster Analysis 9. Principal Components Analysis 10. Market Basket Analysis and Recommendation Engines 11. Time Series and Causality 12. Text Mining A. R Fundamentals Index

Business case

In the upcoming case study, we will apply KNN and SVM to the same dataset. This will allow us to compare the R code and learning methods on the same problem, starting with KNN. We will also spend some time drilling down into the confusion matrix, comparing a number of statistics to evaluate model accuracy.

Business understanding

The data that we will examine was originally collected by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). It consists of 532 observations and eight input features along with a binary outcome (Yes/No). The patients in this study were of Pima Indian descent from South Central Arizona. The NIDDK data shows that since the past 30 years, research has helped scientists to prove that obesity is a major risk factor in the development of diabetes. The Pima Indians were selected for the study as one-half of the adult Pima Indians have diabetes and 95 percent of those with diabetes are overweight. The analysis will focus on adult women...

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