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Data Mining

Data Mining. Overview. Lecture Objectives. After this lecture, you should be able to: Explain key data mining tasks in your own words. Draw an overview of the Data Mining Process . Discuss one broad business application of data mining.

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Data Mining

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  1. Data Mining Overview

  2. Lecture Objectives • After this lecture, you should be able to: • Explain key data mining tasks in your own words. • Draw an overview of the Data Mining Process. • Discuss one broad business application of data mining. • Explain one way to evaluate effectiveness of a Data Mining project.

  3. Data Mining Tasks • Prediction / Classification • Segmentation • Association

  4. Course Overview/Techniques Used • Data Preparation • Prediction/Classification • Discriminant Analysis • Logistic Regression • Artificial Neural Networks • Classification Trees (CART, CHAID) • Segmentation • Judgement • Factor Analysis • Cluster Analysis • Association • Market Basket Analysis • Other Correlation Based techniques

  5. Data Mining ProcessSource: CRISP-DM (SPSS.com website)

  6. Product Planning Customer Acquisition Stage 1 Stage 2 Customer Valuation Customer Manage-ment Collections and Recovery Stage 4 Stage 3 Application in Financial Services

  7. Measuring Effectiveness: Lift/Gains Chart Targeting 100 90 Percent of potential responders captured Random mailing 45 0 45 100 Percent of population targeted Dr. Satish Nargundkar

  8. Discussion Can you think of other applications? What are some limitations of Data Mining? What are future possibilities?

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