Introduction
Through this project we determine whether a person can have a heart disease in future or not?
Dataset
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.
Content
Attribute Information:
1.age 2.sex 3.chest pain type (4 values) 4.resting blood pressure 5.serum cholestoral in mg/dl 6.fasting blood sugar > 120 mg/dl 7.resting electrocardiographic results (values 0,1,2) 8.maximum heart rate achieved 9.exercise induced angina 10.oldpeak = ST depression induced by exercise relative to rest 11.the slope of the peak exercise ST segment 12.number of major vessels (0-3) colored by flourosopy 13.thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
Acknowledgements
Creators:
Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779
Inspiration
Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).
See if you can find any other trends in heart data to predict certain cardiovascular events or find any clear indications of heart health.
Tools Used
I have used pandas, Matplotlib and NumPy for data analysis and manipulation.