A Classification Model that can predict whether or not a person has a heart disease based on physical features of that person (age,sex, cholesterol, etc.)

This model's dataset contains 14 physical attributes based on physical testing of a patient. Blood samples are taken and the patient also conducts a brief exercise test. The "goal" field refers to the presence of heart disease in the patient. It is integer (0 for no presence, 1 for presence). In general, to confirm 100% if a patient has heart disease can be quite an invasive process, so if we can create a model that accurately predicts the likelihood of heart disease, we can help avoid expensive and invasive procedures.

Features:

• age
• sex
• chest pain type (4 values)
• resting blood pressure
• serum cholestoral in mg/dl
• fasting blood sugar > 120 mg/dl
• resting electrocardiographic results (values 0,1,2)
• maximum heart rate achieved
• exercise induced angina
• oldpeak = ST depression induced by exercise relative to rest
• the slope of the peak exercise ST segment
• number of major vessels (0-3) colored by flourosopy
• thal: 3 = normal; 6 = fixed defect; 7 = reversable defect

Label:

• target: 0 for no presence of heart disease, 1 for presence of heart disease

Project by: http://www.pieriandata.com

Original Source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

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.