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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbott
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Learn the art and science of predictive analytics — techniques that get results
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.
- The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
- This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
- Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
- Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
- A companion website provides all the data sets used to generate the examples as well as a free trial version of software
Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.
- Sales Rank: #310208 in Books
- Published on: 2014-04-14
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .88" w x 7.40" l, 1.65 pounds
- Binding: Paperback
- 456 pages
Review
This book provides an excellent background to predictive analytics (BCS, December 2014)
From the Back Cover
APPLY THE RIGHT ANALYTIC TECHNIQUE
Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst shows tech-savvy business managers and data analysts how to use predictive analytics to solve practical business problems. It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish. Internationally recognized data mining and predictive analytics expert Dean Abbott provides a practical and authoritative guide to best practices for successful predictive modeling, including expert tips and tricks to avoid common pitfalls.
This book explains the theory behind the principles of predictive analytics in plain English; readers don’t need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the chapters describes one or more specific techniques and how they relate to the overall process model for predictive analytics. The depth of the description of a technique will match the complexity of the approach, with the intent to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.
Each of the techniques is illustrated by examples, either unique to the task or as part of predictive modeling competitions. The companion website will provide all of the data sets used to generate these examples, along with links to open source and commercial software, so that readers can recreate and explore the examples.
With detailed descriptions of techniques that get results, Applied Predictive Analytics shows you how to:
- Choose the proper analytics technique for various scenarios
- Avoid common mistakes and identify the weaknesses of various techniques
- Mitigate outliers and fill in missing data when necessary
- Interpret predictive models often considered “black boxes,” including model ensembles
- Learn how to assess model performance so the best model is selected
- Apply the appropriate sampling techniques for building and updating models
About the Author
DEAN ABBOTT is President of Abbott Analytics, Inc. (San Diego). He is an internationally recognized data mining and predictive analytics expert with over two decades experience in fraud detection, risk modeling, text mining, personality assessment, planned giving, toxicology, and other applications. He is also Chief Scientist of SmarterRemarketer, a company focusing on behaviorally- and data-driven marketing and web analytics.
Most helpful customer reviews
21 of 21 people found the following review helpful.
Comprehensive Approach
By Keith McCormick
I’ve read dozens of books on data mining. I’m also lead author on a data book that specifically uses IBM SPSS Modeler. Full disclosure: the author of this book and I coauthored the book about Modeler.
This book takes a unique, and badly needed, approach to the subject. It is a “how-to” without being a software book. Too many software instruction books focus so much on features and functions that you lose sight of the big picture. Also, too many data mining books focus solely on algorithms – often one chapter per algorithm. While many of those books are good, and necessary, there are plenty of them already.
This book invests approximately equal coverage to the six phases of the Cross Industry Standard Process for Data Mining (CRISP-DM). The evidence that the author is an expert is easy to find. Rather than merely providing the usual boilerplate on statistical significance, he reminds the reader that data miners interpret the ability of their model to generalize differently and with different tools. Rather than writing a section on regression right out of a introductory statistics book, he shows how he sometimes uses regression for classification, an approach that is technically against the rules. Rather than just a laundry list of algorithms he dedicates an entire chapter to ensembles, describing it not as another algorithm, but as a way of thinking about problems. His descriptions of boosting and bagging are clear and succinct. The essence of the book is in someways captured by the fact that one brief section is entitled “Models Ensembles and Occam’s Razor,” a section that praises ensembles even though they seem to threaten parsimony.
Perhaps, most importantly, he gives lots of advice. A book like this, on a topic like this, can be overwhelming in its factual detail. Knowledge of how the technique works does not imply action in and of itself. You need to know what you should do with this information. Applied Predictive Analytics is a coaching and mentoring session with someone that has been doing it for more than 20 years.
19 of 21 people found the following review helpful.
Some improvements could make this a better book
By OnceMore
I like to compare this book with a very similar one from O'Reilly, entitled "Data Science for Business" by Foster Provost and Tom Fawcett.
Both books are organized around the Cross-Industry Standard Process Model for Data Mining (CRISP-DM), which groups data mining / predictive analytics project tasks into the following six distinct stages:
* Business Understanding: Define the project (e.g., what are the business and data modeling objectives, how are they aligned, what would be the target and/or input variables, what criteria would be used for evaluating the models, how would the models be deployed, etc)
* Data Understanding: Examine the data; identify potential problems with the data
* Data Preparation: Fix problems in the data (e.g., decide what to do with outliers and missing values; standardize data formats etc.); create derived variables; transform and/or normalize data
* Modeling: Build predictive or descriptive models
* Evaluation: Assess models; report on the expected effects of models
* Deployment: Use the models; monitor model performance
In terms of coverage, this book provides guidance for all of the above-mentioned stages, with particular attention to the Data Understanding, Data Preparation, and Evaluation stages, while the Provost and Fawcett book provides guidance mostly for the Business Understanding, Data Understanding, Modeling, and Evaluation stages only.
This book covers more modeling algorithms than the Provost and Fawcett book, but both books tend to keep discussions of the covered algorithms to qualitative descriptions only, instead of the in-depth mathematical discussions found in more theoretically-oriented books. The Provost and Fawcett book does provide better and slightly deeper descriptions of the covered algorithms, however.
Both books cover Decision Trees, for example. Whereas this book only mentions that Decision Trees belong to a class of recursive partitioning algorithms that use concepts such as "Information Gain" or "Gini Index" as possible partitioning criteria, the Provost and Fawcett book goes further by illustrating how "Information Gain" can actually be computed using simple formulas and a small enough but still interesting dataset. By learning how to hand-build the resulting Decision Tree from scratch using the illuminating but still simple example, readers of that book are likely to have more memorable insights about Decision Trees than those they can acquire from this book.
Compared to the Provost and Fawcett book, which I think is the better book pedagogically speaking because it does more things right for its readers, this book could also probably benefit from the following suggested improvements:
* Be more selective regarding what gets discussed in the Business Understanding chapter. Although it is true that the project plan that is being drawn at this stage should include deliberations about how models are going to get evaluated, a few statements indicating this should suffice. There is probably no need to make specific mentions of things such as Lift, Gain, ROC, Area Under the Curve, and Confusion Matrices which don't really get defined and discussed much, much later in the book. By doing so, the author is being laudably meticulous but risks unnecessarily distracting his readers to details they might not yet be equipped to process.
* Reconsider the ordering of some chapters. An earlier modeling chapter discusses Kohonen Self-Organizing Maps (SOM)-- a type of neural networks -- before the more basic neural networks discussion takes place in a later chapter. A chapter on how to interpret Clusters discusses the use of Decision Trees for this purpose before Decision Trees themselves are discussed in a later chapter. Having now read the book, I think reversing those chapters would make the book more readable for those who may not yet know much about neural networks or decision trees.
* Consider using color graphics. Some texts in the book read as though readers were looking at color graphics but in print those graphics were actually in grey-scale.
For readers interested in knowing what modeling algorithms are covered in this book, they include: Itemsets and Association Rules (or Market Basket Analysis), Principal Components Analysis, Clustering (K-Means and Kohonen SOM), Decision Trees, Logistic Regression, Neural Networks, K-Nearest Neighbor, Naive Bayes, Linear Regression, and Model Ensembles (Bagging, Boosting, etc.).
Finally, for readers curious about possible prerequisites for this book, I would say they include basic knowledge of statistics including understanding of concepts such as z-scores, correlations, and ANOVA (Analysis of Variance), and some SQL concepts such as group by and where clauses. The more knowledge you have of these concepts, the easier time you would have reading this book.
10 of 10 people found the following review helpful.
Different books for different people
By Someone
[Good]
There are few books on predictive analytics. Most of them are either university textbooks with prices to match or encompass other areas of analytics such as classification (which isn't a bad thing).
[Ok]
It's not really a pure introduction to subject, nor is it a definitive guide. You will need some context and some really basic knowledge of statistics. This will not satisfy either the expert or the beginner.
[Bad]
There are no exercises for not forcing the reader apply and test knowledge. It's really hard to gauge your understanding of a subject, when you're just being spoon fed content.
[Verdict]
It's a good starter for someone who's vaguely familiar with analytics in general, who needs some light knowledge very quickly.
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