Descriptive Modeling: It uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorizing customers by product preferences or sentiment. Sample techniques include:
Predictive Modeling: This modeling goes deeper to classify events in the future or estimate unknown outcomes for example, using credit scoring to determine an individual's likelihood of repaying a loan. Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaults. Sample techniques include:
Prescriptive Modeling: With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.
In the end, you should not look at data mining as a separate, standalone entity because pre-processing (data preparation, data exploration) and post-processing (model validation, scoring, model performance monitoring) are equally essential. Prescriptive modelling looks at internal and external variables and constraints to recommend one or more courses of action for example, determining the best marketing offer to send to each customer. Sample techniques include:
Read this article:Read More..