Over 60 practical recipes to achieve better results using the experts' methods for data mining Overview In Detail IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide. What you will learn from this book Approach This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts. Who this book is for If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.
Keith McCormick is an independent data mining consultant, trainer, and author. He's been using SPSS Statistics and SPSS Modeler for many years, but is interested in all aspects of Data Mining, Statistics, and Predictive Analytics.
I just went through chapter 1 and found it a great journey to go through each example. Of course, for doing so I had to download the huge file from the website – but it pays off well. The recipes are provided with a form of rigor, and at the same time appropriate precautions on using these recipes are given (which I love). The description of each example is detailed just enough to keep me move along without becoming tedious… So far I've liked it very much, and I also enjoy the way how each example is given as a preview of more elaborated illustration in later chapters, so that I know what I shall be looking forward to when going further with this book.
There are a few neat things that someone might not figure out for themselves. This book provides a good indication of how well using canned software packages like modeler or e-miner are at teaching predictive analytics, but also how unscalable they would be for large enterprise solutions that require more sophisticated customization, and efficient code design. I'm not sure about E-miner but Modeler has horrible memory leaks and gives errors that provide you no guidance for determining what the problem really is so you can account for it in actual code. The best thing about Modeler is you can incorporate R and Python.
This book guides you through why and how to do the right stats test for your data, using SPSS using everyday examples and step-by-step instructions so you can understand the results. Written in an easy style you can learn lots of practical skills about a specific test quickly without having to have read every preceding chapter. Having read lots of stats books this is the easiest to use to get useful results quickly without having to becoming a stats expert beforehand. http://bit.ly/1abrhkI