Cardiovasvular Risk Prediction

  • Tech Stack: Python, Jupyter, NumPy, Pandas, matplotlib, Seaborn, Plotly
  • Github URL: Project Link

  1. The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables each attribute is a potential risk factor. There are demographic, behavioral, and medical risk factors.

  2. There is an imbalance between the classes; class 1 has only 15.1% of the data.

  3. Logistic Regression provided the best accuracy of 85% and Random Forest provided the best one following it of 84.5%.

  4. The peoples of age above 55 have high risk of contracting disease and the risk increases with age.