
Mastering Predictive Analytics with R
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
Rui Miguel Forte(Author)
Packt Publishing
Published on 17. June 2015
Book
Paperback/Softback
414 pages
978-1-78398-280-6 (ISBN)
Description
Key Features
Book DescriptionThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.What you will learn
Master the steps involved in the predictive modeling process
Learn how to classify predictive models and distinguish which models are suitable for a particular problem
Understand how and why each predictive model works
Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
Select appropriate metrics to assess the performance of different types of predictive model
Diagnose performance and accuracy problems when they arise and learn how to deal with them
Grow your expertise in using R and its diverse range of packages
Who this book is forThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
Book DescriptionThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.What you will learn
Master the steps involved in the predictive modeling process
Learn how to classify predictive models and distinguish which models are suitable for a particular problem
Understand how and why each predictive model works
Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
Select appropriate metrics to assess the performance of different types of predictive model
Diagnose performance and accuracy problems when they arise and learn how to deal with them
Grow your expertise in using R and its diverse range of packages
Who this book is forThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 22 mm
Weight
769 gr
ISBN-13
978-1-78398-280-6 (9781783982806)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Rui Miguel Forte
Mastering Predictive Analytics with R
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
E-Book
06/2024
1st Edition
Packt Publishing Limited
from
€42.09
Available for download
Person
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.
Content
Table of Contents
Gearing Up for Predictive Modeling
Linear Regression
Logistic Regression
Neural Networks
Support Vector Machines
Ensemble Methods
Tree-Based Methods
Graphical Models
Time Series Forecasting
Topic Modeling
Recommendation Systems
Gearing Up for Predictive Modeling
Linear Regression
Logistic Regression
Neural Networks
Support Vector Machines
Ensemble Methods
Tree-Based Methods
Graphical Models
Time Series Forecasting
Topic Modeling
Recommendation Systems