Heart disease is the number one cause of death worldwide, so if you're looking to use data science for good you've come to the right place. To learn how to prevent heart disease we must first learn to reliably detect it.

Our dataset is from a study of heart disease that has been open to the public for many years. The study collects various measurements on patient health and cardiovascular statistics, and of course makes patient identities anonymous.

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.

People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Task 1:-Prepare a complete data analysis report on the given data.

Task 2:- Create a model predicting potential Heart Diseases in people using Machine Learning algorithms.

Task3:-Suggestions to the Hospital to awake the predictions of heart diseases prevent life threats.