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Typical Data Mining Process
for Predictive Modeling

The following diagram depicts the overall process for a typical data mining project in which we are doing predictive modeling using techniques such as linear or logistic regression, nominal or ordinal CHAID, etc. As the diagram shows, we begin by defining the business problem to be solved. Then we go through a process of data discovery, which might include examining not only the client's proprietary data, but also available syndicated demographic and lifestyle data that we may be able to overlay onto the client's data to enhance the information.

Before we begin the predictive modeling process, we split the data sample in half. One half will be used to build the predictive model; the other half will be used as a "holdout" sample on which to validate the predictive model. Finally, we apply the model parameters to the client's master data file using a scoring algorithm. This will allow the client to achieve more profitable customer prospecting or customer cross-sell/up-sell.

predictive modeling diagram showing overall process: problem definition, data acquisition, model creation, model validation using a holdout sample, and model implementation

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