1 / 30

Contraceptive Method Choice

Contraceptive Method Choice. 指導教授 黃三益博士 組員 :B924020007 王俐文 B924020009 謝孟凌 B924020014 陳怡珺. Background and Motivation. Population of the world increases tremendously, people of present day pay more attention to contraceptive method.

morton
Download Presentation

Contraceptive Method Choice

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Contraceptive Method Choice 指導教授 黃三益博士 組員:B924020007 王俐文 B924020009 謝孟凌 B924020014 陳怡珺

  2. Background and Motivation • Population of the world increases tremendously, people of present day pay more attention to contraceptive method.

  3. Step one: Translate the Business Problem into a Data Mining Problem • Topic: Contraceptive Method Choice • Predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. • Especially what kind of couples would chose long-term method.

  4. Step two: Select Appropriate Data • Title: Contraceptive Method Choice • Sources: • Origin: Subset of the 1987 National Indonesia Contraceptive Prevalence Survey • Creator: Tjen-Sien Lim • Date: June 7, 1997

  5. Step two: Select Appropriate Data • Number of Instances: 1473 • There is no missing value in this dataset.

  6. Step two: Select Appropriate Data • Number of attributes: 10 (including the class attribute)

  7. Step three: Get to Know the Data • Attribute Information

  8. Step three: Get to Know the Data • Attribute Information

  9. Step three: Get to Know the Data • Attribute Information

  10. Step three: Get to Know the Data • Attribute Information

  11. Step three: Get to Know the Data • Attribute Information

  12. Step three: Get to Know the Data • Attribute Information

  13. Step three: Get to Know the Data • Attribute Information

  14. Step three: Get to Know the Data • Attribute Information

  15. Step three: Get to Know the Data • Attribute Information

  16. Step three: Get to Know the Data • Attribute Information

  17. Step Four : Create a Model Set • Raw Data

  18. Step Four : Create a Model Set • Total 1473 samples • 75% of the data as training set the rest of the data as testing set →By random sampling • Rapid Miner

  19. Step Five: Fix Problems with the Data • No missing value • Skewed distributions

  20. Step Six : Transform Data to Bring Information to the Surface • most of the values of the attribute named Media Exposure are “Good” • the numeric variables to do the statistical analysis to finding outliers

  21. Step7 Build Model • By RapidMiner, build it with Decision Tree

  22. Step7 Build Model(con’t)

  23. Step7 Build Model(con’t) • Ripper Rule • if wife_age > 30 and Num_children_born <= 1 then 1 (53 / 1 / 3) • if Num_children_born <= 0 then 1 (36 / 0 / 0) • if Wife_education = 4 and wife_age <= 42 and Wife_religion = 0 and Num_children_born > 3 then 2 (0 / 14 / 0) • if Wife_education = 1 and Husband_occupation = 2 then 1 (17 / 0 / 1) • if Wife_education = 4 and wife_age > 33 and Num_children_born > 2 and Husband_occupation = 1 and Num_children_born <= 3 then 2 (1 / 10 / 2)

  24. Step7 Build Model(con’t) • if Num_children_born > 2 and wife_age <= 33 and Wife_now_working = 1 and Num_children_born <= 4 and wife_age > 28 then 3 (1 / 0 / 13) • if wife_age <= 35 and Num_children_born > 4 and Media_exposure = 0 then 3 (1 / 2 / 12) • if Husband_education = 4 and wife_age <= 44 and living_level = 3 and wife_age > 37 then 2 (0 / 5 / 0) • else 1 (305 / 168 / 281)

  25. Step7 Build Model(con’t) • Weka-JRip • (Wife_education = 4) and (Num_children_born >= 3) and (wife_age >= 35) => method_used=2 (178.0/76.0) • (wife_age <= 33) and (Num_children_born >= 3) => method_used=3 (271.0/120.0) • (wife_age <= 33) and (Num_children_born >= 1) and (wife_age <= 22) => method_used=3 (106.0/51.0) • => method_used=1 (771.0/342.0)

  26. Step 8 Assess Model • Decision Tree

  27. Step 8 Assess Model(con’t) • Ripper Rule

  28. Step 8 Assess Model(con’t) • JRip Rule

  29. Conclusion • Result • The problems we should improve • more data • ignore some attributes • details of the attribute are not so clear • period and environment have changed

  30. Thanks for you listening…

More Related