1. Show Me Potential Customers Data Mining Approach Leila Etaati

2. 10 years experience in SQL server PhD students in Information System Department, Business School University of Auckland Lecturer and Tutor of BI and database System design in University of Auckland @@Leila_Etaati Leila.etaati@gmail.com www.rad.pasfu.com www.linkedin.com/in/www.linkedin.com/in/leilaetaat i

3. Solving Real-World Challenges with MS Data Mining (DEMO) Descriptive AnalysisPredictive AnalysisEnhance.NET Application with Data Mining Exploring Data Mining Algorithms Introduction to Microsoft Data Mining Solution Introduction to Data Mining Agenda

4. Introduction to Data Mining

6. Data mining : Marketing Department

7. DM Types of Analysis?

8. Data Mining Life Cycle

9. Introduction to Microsoft Data Mining Solution

10. Mining Structure

11. Train and Test

13. Microsoft Decision Tree Algorithm A descriptive and predictive algorithm Accept both discrete and continues attributes, needs a key column, input column It employs feature selection technique to guide the selection Who is going to buy the bike Age>40Have 0 child Have 1-2 Childs Have 3 or more Childs Age<=40Have 0 Childs Have 1-2 Childs Have 3 or more Childs Age Number of Child at home

14. Clustering Algorithm  Categorize items in groups with similar attribute values  employs K-means algorithm and Expectation Maximization (EM)  Mostly descriptive but can be predictable

15. Microsoft Naïve Bayes Algorithm A classification Algorithm Uses Bayesian technique for categorization. It is useful for finding attributes that effects on generating a result, such as finding prospective buyers of a product.(descriptive)

16. Association Rule Identifies association between attributes. One of the most common usage of this is to do a market basket analysis with this algorithm

17. Time Series For time based analysis. Such as predicting sales for next couple of months.

19. Bike Buyers Number of Car at Home Buying the Bike

20. Demo: Microsoft Decision Tree, Clustering and Naïve Bayes Leila Etaati

22. Content Type Discrete data values are separate such as colour values: Red, Yellow, and Blue Continuous data values are continues; such as Age, or salary. Cyclical data values are in a cyclic order, such as days of week. Ordered data values are in a sequential order; such as days of month. Discretized data values are continues, but bucketed into categories and as a result behave as discrete.

23. Accuracy Charts Lift Chart Profit Chart Classification Matrix Cross Validation

24. Demo: Finding the Best Algorithm Leila Etaati

25. Demo: Prediction with DMX Leila Etaati

26. DEMO: Enhance.NET Application with Data Mining

27. CREATE MINING MODEL TravelBudgetPrediction ( traveller_ID long KEY, Year TEXT DISCRETE, Quarter TEXT DISCRETE, mode TEXT DISCRETE, country TEXT DISCRETE, purpose TEXT DISCRETE, package TEXT DISCRETE, Age TEXT DISCRETE, Sex TEXT DISCRETE, Duration TEXT DISCRETE, Visits long DISCRETE, Nights long DISCRETE, Spend long DISCRETE PREDICT) USING MICROSOFT_DECISIONTREE;

28. DMX Code with.Net (predict the travel Budget)

29. Demo: Microsoft Association Rule Leila Etaati

31. Summary Solving Real-World Challenges with MS Data Mining (DEMO) Descriptive AnalysisPredictive AnalysisEnhance.NET Application with Data Mining Exploring Data Mining Algorithms Introduction to Microsoft Data Mining Solutions Introduction to Data Mining

32. References to Study More  Data Mining Tutorials in MSDN: http://technet.microsoft.com/en-us/library/bb677206.aspx  Data Mining Algorithms in MSDN: http://technet.microsoft.com/en-us/library/ms175595.aspx  Data Mining with SQL Server 2008 Book: http://www.amazon.com/Data-Mining-Microsoft-Server-2008/dp/0470277742

33. @@Leila_Etaati Leila.etaati@gmail.com www.rad.pasfu.com www.linkedin.com/in/www.linkedin.com/in/leilaetaati

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