Prediction of Heart disease onset using patient data

The following report entails the contents of the second and final Data Science project that I finished, as part of the HarvardX PH125.9x Data Science: Capstone final course. For this project, I created a machine-learning algorithm that predicts if a person will, or will not get Heart Disease, based on selected parameters. According to 2019 worldwide statistics, 18.6 million people died of cardiovascular disease, globally, marking a 17.1% increase in the number of cases over the last decade; which makes this research-topic very challenging yet interesting, from the view of a data-scientist. A number of research papers have been published on this topic, and the most popular models used were K-Nearest-Neighbours, Naive Beyes and the Random Forest model.