Data Mining for Business Analytics: Concepts, Techniques, by Galit Shmueli

By Galit Shmueli

Info Mining for enterprise Analytics: thoughts, strategies, and purposes in XLMiner®, 3rd version offers an utilized method of information mining and predictive analytics with transparent exposition, hands-on routines, and real-life case reviews. Readers will paintings with the entire general facts mining tools utilizing the Microsoft® place of work Excel® add-in XLMiner® to enhance predictive versions and receive company worth from substantial facts. that includes up to date topical assurance on textual content mining, social community research, collaborative filtering, ensemble tools, uplift modeling and extra. info Mining for company Analytics: recommendations, options, and functions in XLMiner®, 3rd variation is a perfect textbook for upper-undergraduate and graduate-level classes in addition to specialist courses on information mining, predictive modeling, and large facts analytics. the recent version can also be a different reference for analysts, researchers, and practitioners operating with predictive analytics within the fields of industrial, finance, advertising, laptop technology, and knowledge expertise.

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Com team, led by Kuber Deokar, Instructional Operations Supervisor, Dhanashree Vishwasrao, Assistant Teacher, and, with special gratitude for her hours of doublechecking XLMiner, Shweta Jadhav. xxxi PART I PL"· . re lmlnaneS 1 ® ○ 4 INTRODUCTION WHO USES PREDICTIVE ANALYTICS? ac users than Windows users. • Staples online store found it could charge more for staplers if a customer lived far from a Staples store. Beware the organizational setting where analytics is a solution in search of a problem: A manager, knowing that business analytics and data mining are hot areas, decides that her organization must deploy them too, to capture that hidden value that must be lurking somewhere.

In such cases, we would want our sampling procedure to overweight the rare class (purchasers or frauds) relative to the majority class (nonpurchasers, nonfrauds) so that our sample would end up with a healthy complement of purchasers or frauds. Assuting an adequate nwnber of responder or "success" cases to train the model is just part of the picture. A more important factoris the costs of misclassification. Whenever the response rate is extremely low, we are likely to attach more importance to identifying a responder than to identifYing a nonresponder.

Most large organizations face both the challenge and the opportunity of big data because most routine data processes now generate data that can be stored and, possibly, analyzed. , 15 variables and 5000 records) to the Walmart database. If you consider the traditional statistical study to be the size of a period at the end of a sentence, then the Walmart database is the size of a football field. And that probably does not include other data associated with Wa1mart-social media data, for example, which comes in the form of unstructured text.

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