By Galit Shmueli
Facts Mining for enterprise Analytics: innovations, strategies, and functions in XLMiner®, 3rd version provides an utilized method of facts mining and predictive analytics with transparent exposition, hands-on routines, and real-life case reviews. Readers will paintings with the entire ordinary info mining equipment utilizing the Microsoft® place of work Excel® add-in XLMiner® to strengthen predictive types and the best way to receive company worth from large info. that includes up to date topical insurance on textual content mining, social community research, collaborative filtering, ensemble equipment, uplift modeling and extra. info Mining for company Analytics: options, thoughts, and purposes in XLMiner®, 3rd version is a perfect textbook for upper-undergraduate and graduate-level classes in addition to expert courses on info mining, predictive modeling, and large info analytics. the hot version can also be a special reference for analysts, researchers, and practitioners operating with predictive analytics within the fields of commercial, finance, advertising, machine technology, and knowledge expertise.
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Additional resources for Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
Com/ xLminer-data-mining and foLLow the instructions there. Installation CLose any ExceL windows, then run the XLMiner setup program. DiaLog boxes wiLL guide you through the instaLlation procedure. The finaL diaLog box gives you an option to start ExceL and open a "Getting Started" workbook. You'll also find XLMiner options under Start > All Programs > Frontline Systems. 3. By choosing the appropriate menu item, you can run any of XLMiner's procedures on the dataset that is open in your ExceL worksheet.
Similarly, Dan Toy and John Elder IV greeted our proj ect with enthusiasm and provided detailed and useful comments on earlier drafts. Boaz Shmueli and Raquelle Azran gave detailed editorial comments and suggestions on the first two editions; Bruce McCullough and Adam Hughes did the same for the first edition. Noa Shmueli provided careful proofs of the third edition. Ravi Bapna, who used an early draft in a data mining course at the Indian School of Business, has provided invaluable comments and helpful suggestions since the book's start.
In direct-response advertising (whether by traditional mail, email or web advertising), we may encounter only one or two responders for every hundred records-the value offmding such a customer far outweighs the costs of reaching him or her. In trying to identifY fraudulent transactions, or customers unlikely to repay debt, the costs of failing to fmd the fraud or the nonpaying customer are likely to exceed the cost of more detailed review of a legitimate transaction or customer. If the costs of fulling to locate responders are comparable to the costs of misidenti£Y:ing responders as nonresponders, our models would usually achieve highest overall accuracy if they identified everyone (or almost everyone, if it is easy to identifY a few responders without catching many nonresponders) as a nonresponder.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner by Galit Shmueli