In this project, we explore the application of various classification models to predict the presence or absence of heart disease based on a dataset acquired from Kaggle: Cleveland UCI Heart Disease Dataset

1. age: age in years
2. sex: sex (1 = male; 0 = female)
3. cp: chest pain type (0: typical angina; 1: atypical angina; 2: non-anginal pain; asymptomatic)
4. trestbps: resting blood pressure (in mm Hg on admission to the hospital)
5. chol: serum cholestoral in mg/dl
6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
7. restecg: resting electrocardiographic results (0: normal; 1:having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV); 2: showing probable or definite left ventricular hypertrophy by Estes' criteria)
8. thalach: maximum heart rate achieved
9. exang: exercise induced angina (1 = yes; 0 = no)
10. oldpeak = ST depression induced by exercise relative to rest
11. slope: the slope of the peak exercise ST segment (0: upsloping; 1: flat; 2: downsloping)
12. ca: number of major vessels (0-3) colored by flourosopy
13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect

condition: 0 = no disease, 1 = disease

• Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
• University Hospital, Zurich, Switzerland: William Steinbr
• 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

We aim to predict heart conditions using different classification models, including K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. These models will help us understand the predictive power of different features and their impact on diagnosing heart disease.

Below is an example of an unpruned Decision Tree model visualized for this task: