It focuses on creating a bayesian network from the data-set (of Coronary Artery Disease) and then predicting data (50 rows) with the help of different prediction techniques by taking reference from the data set already given. We also find the accuracy of the predicted data.
To create bayesian network of the given data-set we use pgmpy libraries. The dataset has total 27 columns and each column is treated as node and dependencies are created.
To run and understand the code, download jupyter notebook and dataset (.csv) and run on platform like google colab.