Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them.