Data Mining Cookbook by Rud O.P.

By Rud O.P.

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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. And on the income axis, the values of 0 for response are clustered around the lower values for income. Conversely, the values of 1 for response are clustered around the higher values for income.

Fraud detection models are assisting banks in reducing losses by learning the typical spending behavior of their customers. If a customer's spending habits change drastically, the approval process is halted or monitored until the situation can be evaluated. Activation Activation models are models that predict if a prospect will become a full -fledged customer. These models are most applicable in the financial services industry. For example, for a credit card prospect to become an active customer, the prospect must respond, be approved, and use the account.

To enhance their data mining efforts, many companies are diligently collecting, combining, and scrubbing data. Companies new to data mining are revamping their databases to allow for easier access and extractability. Companies whose primary business is to collect, enhance, and sell data are springing up every day. All of this can be a bit overwhelming. So, in this chapter, I start at the beginning. The first step in making the best use of any data source is to understand the nature of the data as well as how it is gathered and managed.

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