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Mastering Predictive Analytics with R, Second Edition

You're reading from Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

Product type Paperback

Published in Aug 2017

Publisher Packt

ISBN-13 9781787121393

Length 448 pages

Edition 2nd Edition

Languages

R

Concepts

Data Analysis

Authors (2):

James D. Miller

Rui Miguel Forte

View More author details

Table of Contents (16) Chapters

Preface

1. Gearing Up for Predictive Modeling FREE CHAPTER

2. Tidying Data and Measuring Performance

3. Linear Regression

4. Generalized Linear Models

5. Neural Networks

6. Support Vector Machines

7. Tree-Based Methods

8. Dimensionality Reduction

9. Ensemble Methods

10. Probabilistic Graphical Models

11. Topic Modeling

12. Recommendation Systems

13. Scaling Up

14. Deep Learning

Index

Predicting heart disease

We'll put logistic regression for the binary classification task to the test with a real-world dataset from the UCI Machine Learning Repository. This time, we will be working with the Statlog (Heart) dataset, which we will refer to as the heart dataset henceforth for brevity. The dataset can be downloaded from the UCI Machine Repository's website at http://archive.ics.uci.edu/ml/datasets/Statlog+%28Heart%29. The data contains 270 observations for patients with potential heart problems. Of these, 120 patients were shown to have heart problems, so the split between the two classes is fairly even. The task is to predict whether a patient has a heart disease based on their profile and a series of medical tests. First, we'll load the data into a data frame and rename the columns according to the website:

> heart <- read.table("heart.dat", quote = "\"")
> names(heart) <- c("AGE", "SEX", "CHESTPAIN", "RESTBP", "CHOL", "SUGAR", "ECG", "MAXHR", "ANGINA", "DEP"...

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Authors (2)

James D. Miller

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

See other products by James D. Miller

Rui Miguel Forte

Rui Miguel Forte

Rui Miguel Forte is currently the chief data scientist at Workable. He was born and raised in Greece and studied in the UK. He is an experienced data scientist, having over 10 years of work experience in a diverse array of industries spanning mobile marketing, health informatics, education technology, and human resources technology. His projects have included predictive modeling of user behavior in mobile marketing promotions, speaker intent identification in an intelligent tutor, information extraction techniques for job applicant resumes and fraud detection for job scams. He currently teaches R, MongoDB, and other data science technologies to graduate students in the Business Analytics MSc program at the Athens University of Economics and Business. In addition, he has lectured in a number of seminars, specialization programs, and R schools for working data science professionals in Athens. His core programming knowledge is in R and Java, and he has extensive experience working with a variety of database technologies such as Oracle, PostgreSQL, MongoDB, and HBase. He holds a Master's degree in Electrical and Electronic Engineering from Imperial College London and is currently researching machine learning applications in information extraction and natural language processing.

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