Our editors have compiled this directory of the best predictive modeling books based on Amazon user reviews, rating, and ability to add business value.
There are loads of free resources available online (such as Solutions Review’s Data Analytics Software Buyer’s Guide, visual comparison matrix, and best practices section) and those are great, but sometimes it’s best to do things the old fashioned way. There are few resources that can match the in-depth, comprehensive detail of one of the best predictive modeling books.
The editors at Solutions Review have done much of the work for you, curating this directory of the best predictive modeling books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in this compilation meets a minimum criteria of 10 reviews and a 4-star-or-better ranking.
Below you will find a library of titles from recognized industry analysts, experienced practitioners, and subject matter experts spanning the depths of predictive analytics all the way to data science. This compilation includes publications for practitioners of all skill levels.
Note: Titles with recently published new editions will be included if the previous edition met our review and ranking criteria.
“Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.”
“Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.”
Hands-On Predictive Analytics with Python: Master the complete predictive analytics process, from problem definition to model deployment
“With the help of practical, step-by-step examples, you’ll be able to build predictive analytics solutions while using cutting-edge Python tools and packages. You’ll learn effectively by defining the problem and then moving on to identifying relevant data. As you advance, you’ll get to grips with tasks such as data preparation, exploring and visualizing relationships, building models, and more. You will also work with models such as K-Nearest Neighbors (KNN), random forests, and neural networks using key libraries in Python’s data science stack including NumPy, pandas, Matplotlib, and Seaborn.”
“Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis.”
“Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.”
“The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.”
“Kattamuri Sarma’s Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis.”
Applied Analytics Through Case Studies Using SAS and R: Implementing Predictive Models And Machine Learning Techniques
“Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. “
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