Data Mining Cookbook: Modeling Data for Marketing, Risk and by Olivia Parr Rud

By Olivia Parr Rud

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

In order to discover new how you can enhance buyer revenues and aid, and in addition to deal with hazard, company managers needs to be in a position to mine corporation databases. This e-book presents a step by step consultant to making and enforcing versions of the main frequently asked info mining questions. Readers will methods to organize facts to mine, and boost exact info mining questions. the writer, who has over ten years of knowledge mining adventure, additionally presents genuine validated versions of particular facts mining questions for advertising and marketing, revenues, customer support and retention, and hazard administration. A CD-ROM, bought individually, offers those versions for reader use.

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This technique is very powerful in fitting a binary or two -level outcome such as a response to an offer or a default on a loan. One of the advantages of a neural network is its ability to pick up nonlinear relationships in the data. This can allow users to fit some types of data that would be difficult to fit using regression. One drawback, however, is its tendency to over-fit the data. This can cause the model to deteriorate more quickly when applied to new data. If this is the method of choice, be sure to validate carefully.

This overview details the steps for a successful targeting model project, from conception to implementation. I begin with the most important step in developing a targeting model: establishing the goal or objective. Several sample applications of descriptive and predictive targeting models help to define the business objective of the project and its alignment with the overall goals of the company. Once the objective is established, the next step is to determine the best methodology. This chapter defines several methods for developing targeting models along with their advantages and disadvantages.

This technique is very powerful in fitting a binary or two -level outcome such as a response to an offer or a default on a loan. One of the advantages of a neural network is its ability to pick up nonlinear relationships in the data. This can allow users to fit some types of data that would be difficult to fit using regression. One drawback, however, is its tendency to over-fit the data. This can cause the model to deteriorate more quickly when applied to new data. If this is the method of choice, be sure to validate carefully.

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