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Prediction as Data Mining Task Definition and business-related examples Prepared by Huan Truong Omer Demir.

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Presentation on theme: "Prediction as Data Mining Task Definition and business-related examples Prepared by Huan Truong Omer Demir."— Presentation transcript:

1 Prediction as Data Mining Task Definition and business-related examples Prepared by Huan Truong Omer Demir

2 Outline What is Data Mining? Data Mining – Predict the future Specific uses of Data Mining Predictive Data Mining

3 What is Data Mining Knowledge discovery Computer-assisted process of digging through and analyzing enormous sets of data  Extracting the meaning of the data

4 Data mining – Predict the Future Predict behaviors and future trends Allowing businesses to make proactive, knowledge-driven decisions

5 Data mining – Predict the Future What are the anticipated sales by territory and region for next year? Which salespersons are expected to exceed their quotas next year? For the next two years, which stores are likely to have best performance? What is the expected returns for next year’s promotions? Which customers are likely to switch to the competition next year?

6 Specific uses of data mining Market segmentation Customer churn Fraud detection Direct marketing Interactive marketing Trend analysis …

7 Predictive Data Mining The most common type of data mining The most direct business applications Data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest.

8 Predictive Data Mining Techniques Neural Network - Analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data

9 Predictive Data Mining Techniques Meta-Learner – Combination of the predictions from multiple models, useful when the types of models included in the project are very different


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