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CS548 Showcase Using SPSS for Data Mining

CS548 Showcase Using SPSS for Data Mining. Ahmedul Kabir. References. http://www-01.ibm.com/software/analytics/spss/ http ://en.wikipedia.org/wiki/Spss http :// www.ats.ucla.edu/stat/spss/output/reg_spss.htm (for a good interpretation of the results). What is SPSS .

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CS548 Showcase Using SPSS for Data Mining

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  1. CS548 ShowcaseUsing SPSS for Data Mining Ahmedul Kabir

  2. References • http://www-01.ibm.com/software/analytics/spss/ • http://en.wikipedia.org/wiki/Spss • http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm(for a good interpretation of the results)

  3. What is SPSS • Software Package used for Statistical Analysis of data. • Produced by SPSS Inc. in 1968. • SPSS used to stand for “Statistical Package for the Social Sciences” • Later changed to “Statistical Product and Service Solutions” • Acquired by IBM in 2009. Now known as IBM-SPSS Statistics

  4. More on SPSS software • Current version is 22.0 • SPSS is a commercial software • Statistic 17.0 (basic package) is freely available for WPI students • Several specialized packages can be bought: • SPSS Data Collection (for surveys) • SPSS Modeler (for data mining) • SPSS Analytic Catalyst (for Big data) etc.

  5. File formats • Basic format is .SAV • Supports other common formats such as .XLSX, .CSV, .DAT etc • SPSS syntax file (.SPS) can be used to convert other formats to SPSS format

  6. Reasons for NOT using SPSS • Expensive (at least, not free!) • Basic Package is not tailored for Data Mining. • Heavy software

  7. Why use it then? • Very rich collection of Statistical tests and methods • Outputs an extensive set of metrics and statistically important factors • Support available • Well known in non-CS fields

  8. Demo (Data view)

  9. Demo (Variable view)

  10. Available Features

  11. Available Features

  12. Available Features

  13. Demo (Linear Regression)

  14. Demo (Linear Regression)

  15. Demo (Linear Regression)

  16. Demo (Linear Regression)

  17. Demo Output

  18. Predicted Score = -0.002*Age + 0.004*Level of Education – 0.22*Years with current employer + …..

  19. Thank You! Questions?

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