Predict whether the patient has heart disease or not. This is a binary result. We will experiment with different classification models and see which one yields the most accuracy.
Can a classification model predict whether the patient has heart disease or not ?
The dataset was downloaded from the Kaggle website and consisted of 1026 observations. The predictor Y (Positive or Negative diagnosis of Heart Disease) is determined by 14 features (X):
- age: age in years
- sex: male, female
- cp: chest pain type
- trestbps: resting blood pressure
- chol: serum cholesterol
- fbs: fasting blood sugar
- restecg: resting electrocardiographic results
- thalach: maximum heart rate achieved
- exang: exercise induced angina
- oldpeak: ST depression induced by exercise relative to rest
- slope: the slope of the peak exercise ST segment
- ca: number of major vessels
- thal: number of defect type
- target: disease, no disease
- Pandas
- NumPy
- Sklearn
- Seaborn
- matplotlib
- Tableau
- Flask
- Heroku
- Plotly