By Galit Shmueli
Facts Mining for enterprise Analytics: thoughts, concepts, and functions in XLMiner®, 3rd version provides an utilized method of info mining and predictive analytics with transparent exposition, hands-on routines, and real-life case reviews. Readers will paintings with all the ordinary facts mining tools utilizing the Microsoft® workplace Excel® add-in XLMiner® to strengthen predictive types and the way to receive enterprise price from substantial information. that includes up-to-date topical insurance on textual content mining, social community research, collaborative filtering, ensemble equipment, uplift modeling and extra. facts Mining for enterprise Analytics: ideas, ideas, and functions in XLMiner®, 3rd version is a perfect textbook for upper-undergraduate and graduate-level classes in addition to expert courses on facts mining, predictive modeling, and large facts analytics. the hot variation can be a different reference for analysts, researchers, and practitioners operating with predictive analytics within the fields of commercial, finance, advertising, laptop technological know-how, and data know-how.
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Additional resources for Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
More generally, we want to train our model with the asymmetric costs in mind so that the algorithm will catch the more valuable responders, probably at the cost of" catching" and misclassi£Y:ing more nonresponders as responders than would be the case if we assume equal costs. This subject is discussed in detail in Chapter 5. PRELIMINARY STEPS (1, 2, 3) … 23 24 OVERVIEW OF THE DATA MINING PROCESS � � � �1 , … , �15 6×�×� � � PRELIMINARY STEPS For example, suppose we're trying to predict the total purchase amount spent by customers, and we have a few predictor columns that are coded �1 , �2 , �3 , … , where we don't know what those codes mean.
We might have no explanation but know that the value is wrong-a temperature of178◦ F for a sick person. Or, we might conclude that the value is within the realm of possibility and leave it alone. All these are judgments best made by someone with domain knowledge, knowledge of the particular application being considered: direct mail, mortgage finance, and so on, as opposed to technical knowledge of statistical or data mining procedures. Statistical procedures can do litde beyond identifying the record as something that needs review.
The term predictive analytics is sometimes used to also include data pattern identification methods such as clustering. Data Reduction and Dimension Reduction The performance of data mining algorithms is often improved when the number of variables is limited, and when large numbers of records can be grouped into homogeneous groups. For example, rather than dealing with thousands of product types, an analyst might wish to group them into a smaller number of groups and build separate models for each group.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner by Galit Shmueli