implementing, testing, and documenting Predictive Analytics models using SAS miner
Dataset 1: Heart disease Data Set (Download link)
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The “goal” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0). Use only the following attributes.
1. #3 (age) 2. #4 (sex) 3. #9 (cp) 4. #10 (trestbps) 5. #12 (chol) 6. #16 (fbs) 7. #19 (restecg) 8. #32 (thalach) 9. #38 (exang) 10. #40 (oldpeak) 11. #41 (slope) 12. #44 (ca) 13. #51 (thal) 14. #58 (num) (target variable)
* Download the csv files from links provided in the data descriptions.
* Start a new SAS Enterprise Miner project named Final_name.
* Prepare a presentation and explain your findings in less than 12 slides in a recorded presentation. The presentation should not be longer than 5 minutes
* SAS Enterprise Miner files.