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SAS Enterprise Miner Release 4.3

Importing Base Data. SAS's main drawback is the fact that if any line of data has a null or blank value it will totally disregard the full recordIn this case, if we were unable to manipulate the data, the available records would decrease dramaticallyWe can fight back by recoding the data as will b

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SAS Enterprise Miner Release 4.3

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    1. SAS Enterprise Miner Release 4.3 A brief overview: analysis of the Donor Recapture Case (Case 3)

    2. Importing Base Data SASs main drawback is the fact that if any line of data has a null or blank value it will totally disregard the full record In this case, if we were unable to manipulate the data, the available records would decrease dramatically We can fight back by recoding the data as will be shown in the import step

    3. Base SAS Interface Screen

    4. Importing Charity Data

    5. Text Editor

    6. Text Editor

    7. Importing Charity Data

    8. Importing Charity Data

    9. Starting Enterprise Miner from Base SAS module

    12. Binding Data to Program This is an exasperating activity Even for someone who took a SAS training course in Enterprise Miner The documentation is pathetic Ill document each step carefully in case this ever happens to you

    13. Name Project Charity and Drag Input Data Node to Workspace

    14. Bind Data to Project

    15. Bind Data to Project

    16. Bind Data to Project

    17. Bind Data to Project

    18. Change to Larger Sample

    19. Success!

    20. Click Variables Tab

    21. Then Bad Things Happen Who knows why. If I hadnt taken the course the slides would stop here. Thats the only reason I know what to do Ill document this also, in case it happens to you.

    22. Crash Recovery

    23. Crash Recovery

    24. Analysis Resumes Well have a look at MAILCODE. Enterprise Miner has some neat graphical tools that are easy to use. The simplest and easiest are part of the data input tool.

    25. A Histogram

    26. Histogram of Mailcode

    27. Must Identify TARGET_D as Target

    28. Histogram of Target

    29. Save changes!

    30. Add Data Partition Node

    31. This is What it Does

    32. Design Philosophy

    33. Regression

    34. Regression

    35. Regression

    36. Regression

    37. Regression

    38. Regression

    39. Regression

    40. Regression

    41. Regression

    42. Regression

    43. Regression

    44. Regression

    45. Regression

    46. Regression

    47. Regression

    48. Regression

    49. Regression

    50. Regression

    51. Regression

    52. Moving On, Try a Tree

    55. Moving On, Try a Neural Net

    58. Assessment Tool The assessment tool is supposed to give lift charts. Apparently it only does so for binary response. The menu item is blank for predictive models. The tool is good for easily comparing varying model error rates.

    59. Assessment Tool

    60. Assessment Tool

    61. Assessment Tool

    62. Done! The intention was to illustrate the interface, not assess the SASs Enterprise Miner per se. With more effort to fix the missing values problems on input, better results can surely be achieved. With more experience, many of the false steps would not have occurred.

    63. Looping and Control SASs biggest deficiency is the lack of looping and control structures. This affects all of SAS, not just Enterprise Miner. Any data manipulation, such as fixing missing values, must be done by hand, one variable at a time. R has a huge advantage here!

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