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Data mining refers to the study of data (usually by software without human intervention) that is generated by user behavious on the internet. For example, a visit to Amazon.com and a look at books on anthropology will probably trigger amazon's software to flag one as someone interested in anthropolgy. This data is then used across the user's net experience on sites like Facebook, Game sites, etc., to show the user ads related to resources on anthropology. This is done through the use of "cookies" that are placed on the user's computer that can then be read by sites that partner with the cookie-placing site to show relevant ads.

While data mining is the first step in collecting user data, showing ads related to what the user was browsing can be redundant. Going by the previous example, if a user has already bought a book on basic anthropology, it makes little sense to show ads for the very same book. Predictive modeling goes a step or two further. Given that the user has already bought a book on basic anthropology, predictive modeling seeks to predict what the user will most likely need next and then to show ads for those products or services.

Predictive modeling uses much more of the data mined, such as the user's age, gender, known experience in the field, other related interests, etc., to build a model to predict future needs and to thus show ads tailored to those needs.

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Q: What are data mining and predictive modeling?
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What is predictive data mining?

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CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING


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