By Olivia Parr Rud

Increase gains and decrease expenditures through the use of this selection of versions of the main frequently asked information mining questions

In order to discover new how you can enhance client revenues and help, and in addition to deal with possibility, company managers needs to be in a position to mine corporation databases. This publication presents a step by step advisor to making and enforcing types of the main frequently asked facts mining questions. Readers will find out how to arrange facts to mine, and strengthen exact facts mining questions. the writer, who has over ten years of knowledge mining event, additionally offers genuine established types of particular information mining questions for advertising, revenues, customer support and retention, and chance administration. A CD-ROM, offered individually, presents those versions for reader use.

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Additional resources for Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management

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My goal in this section is to avoid heavy statistical jargon. But because this is the primary method used in the book, I am including a thorough explanation of the methodology. Keep in mind that it is very similar to linear regression in the actual model processing. 5, the graph displays a relationship between response (0/1) and income in dollars. The goal is to predict the probability of response to a catalog that sells high-end gifts using the prospect's income. Notice how the data points have a value of 0 or 1 for response.

Genetic Algorithms Similar to neural networks, genetic algorithms do not have an underlying distribution. " Simply put, many models are compared and adjusted over a series of iterations to find the best model for the task. There is some variation among methods. In general, though, the models are altered in each step using mating, mutation, and cloning. As with all modeling methods, the first step is to determine the objective or goal of the model. Then a measure is selected to evaluate model fit.

Let's say we want to find the best model for predicting balances. We use R-square to determine the model fit. 7 we have a group of models that represent the "first generation" of candidate models. These were selected at random or created using another technique. Each model is tested for its ability to predict balances. It is assigned a value or weight that reflects its ability to predict balances in comparison to its competitors. In the right-hand column, we see that the "% of Total" is calculated by dividing the individual R -square by the sum of the R-squares.

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