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Forte Mastering predictive analytics with R: master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
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Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Preface -- Chapter 1: Gearing Up for Predictive Modeling -- Models -- Learning from data -- The core components of a model -- Our first model: k-nearest neighbors -- Types of models -- Supervised, unsupervised, semi-supervised, and reinforcement learning models -- Parametric and nonparametric models -- Regression and classification models -- Real time and batch machine learning models -- The process of predictive modeling -- Defining the models objective -- Collecting the data -- Picking a model -- Pre-processing the data -- Exploratory data analysis -- Feature transformations -- Encoding categorical features -- Missing data -- Outliers -- Removing problematic features -- Feature engineering and dimensionality reduction -- Training and assessing the model -- Repeating with different models and final model selection -- Deploying the model -- Performance metrics -- Assessing regression models -- Assessing classification models -- Assessing binary classification models -- Summary -- Chapter 2 : Linear Regression -- Linear regression -- Assumptions of linear regression -- Simple linear regression -- Estimating the regression coefficients -- Multiple linear regression -- Predicting CPU performance -- Predicting the price of used cars -- Assessing linear regression models -- Residual analysis -- Significance tests for linear regression -- Performance metrics for linear regression -- Comparing different regression models -- Test set performance -- Problems with linear regression -- Multicollinearity -- Outliers -- Feature selection -- Regularization -- Ridge regression -- Least absolute shrinkage and selection operator (lasso) -- Implementing regularization in R -- Summary -- Chapter 3 : Logistic Regression.;This 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.

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Mastering Predictive Analytics with R

Mastering Predictive Analytics with R

Copyright 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: June 2015

Production reference: 1100615

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78398-280-6

www.packtpub.com

Credits

Author

Rui Miguel Forte

Reviewers

Ajay Dhamija

Prasad Kothari

Dawit Gezahegn Tadesse

Commissioning Editor

Kartikey Pandey

Acquisition Editor

Subho Gupta

Content Development Editor

Govindan Kurumangattu

Technical Editor

Edwin Moses

Copy Editors

Stuti Srivastava

Aditya Nair

Vedangi Narvekar

Project Coordinator

Shipra Chawhan

Proofreaders

Stephen Copestake

Safis Editing

Indexer

Priya Sane

Graphics

Sheetal Aute

Disha Haria

Jason Monteiro

Abhinash Sahu

Production Coordinator

Shantanu Zagade

Cover Work

Shantanu Zagade

About the Author

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 who has 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 include the 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. Currently, he 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 at 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.

Acknowledgments

Behind every great adventure is a good story, and writing a book is no exception. Many people contributed to making this book a reality. I would like to thank the many students I have taught at AUEB, whose dedication and support has been nothing short of overwhelming. They should be rest assured that I have learned just as much from them as they have learned from me, if not more. I also want to thank Damianos Chatziantoniou for conceiving a pioneering graduate data science program in Greece. Workable has been a crucible for working alongside incredibly talented and passionate engineers on exciting data science projects that help businesses around the globe. For this, I would like to thank my colleagues and in particular, the founders, Nick and Spyros, who created a diamond in the rough. I would like to thank Subho, Govindan, Edwin, and all the folks at Packt for their professionalism and patience. To the many friends who offered encouragement and motivation I would like to express my eternal gratitude. My family and extended family have been an incredible source of support on this project. In particular, I would like to thank my father, Libanio, for inspiring me to pursue a career in the sciences and my mother, Marianthi, for always believing in me far more than anyone else ever could. My wife, Despoina, patiently and fiercely stood by my side even as this book kept me away from her during her first pregnancy. Last but not least, my baby daughter slept quietly and kept a cherubic vigil over her father during the book's final stages of preparation. She helped in ways words cannot describe.

About the Reviewers

Ajay Dhamija is a senior scientist working in Defense R&D Organization, Delhi. He has more than 24 years' experience as a researcher and instructor. He holds an MTech (computer science and engineering) degree from IIT, Delhi, and an MBA (finance and strategy) degree from FMS, Delhi. He has more than 14 research works of international repute in varied fields to his credit, including data mining, reverse engineering, analytics, neural network simulation, TRIZ, and so on. He was instrumental in developing a state-of-the-art Computer-Aided Pilot Selection System (CPSS) containing various cognitive and psychomotor tests to comprehensively assess the flying aptitude of the aspiring pilots of the Indian Air Force. He has been honored with the Agni Award for excellence in self reliance, 2005, by the Government of India. He specializes in predictive analytics, information security, big data analytics, machine learning, Bayesian social networks, financial modeling, Neuro-Fuzzy simulation and data analysis, and data mining using R. He is presently involved with his doctoral work on Financial Modeling of Carbon Finance data from IIT, Delhi. He has written an international best seller, Forecasting Exchange Rate: Use of Neural Networks in Quantitative Finance (http://www.amazon.com/Forecasting-Exchange-rate-Networks-Quantitative/dp/3639161807), and is currently authoring another book on R named Multivariate Analysis using R .

Apart from analytics, Ajay is actively involved in information security research. He has associated himself with various international and national researchers in government as well as the corporate sector to pursue his research on ways to amalgamate two important and contemporary fields of data handling, that is, predictive analytics and information security.

You can connect with Ajay at the following:

LinkedIn: ajaykumardhamija

ResearchGate: Ajay_Dhamija2

Academia: ajaydhamija

Facebook: akdhamija

Twitter: akdhamija

Quora: Ajay-Dhamija

While associating with researchers from Predictive Analytics and Information Security Institute of India (PRAISIA @ www.praisia.com) in his research endeavors, he has worked on refining methods of big data analytics for security data analysis (log assessment, incident analysis, threat prediction, and so on) and vulnerability management automation.

I would like to thank my fellow scientists from Defense R&D Organization and researchers from corporate sectors such as Predictive Analytics & Information Security Institute of India (PRAISIA), which is a unique institute of repute and of its own kind due to its pioneering work in marrying the two giant and contemporary fields of data handling in modern times, that is, predictive analytics and information security, by adopting custom-made and refined methods of big data analytics. They all contributed in presenting a fruitful review for this book. I'm also thankful to my wife, Seema Dhamija, the managing director of PRAISIA, who has been kind enough to share her research team's time with me in order to have technical discussions. I'm also thankful to my son, Hemant Dhamija, who gave his invaluable inputs many a times, which I inadvertently neglected during the course of this review. I'm also thankful to a budding security researcher, Shubham Mittal from MakeMyTrip, for his constant and constructive critiques of my work.

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