Predicting Heart Failure With Machine Learning

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.

Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.

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.


Customer Segmentation with K-Means Clustering

Customer segmentation is dividing your customers into groups with similar tastes or buying patterns. It can be done by gathering customer buying habits, or with responses to surveys etc. A typical question that can be answered through customer segmentation is how a new product should be positioned or offered when it’s launched.

In the following example we were provided with a customer database from a wine merchant that had tracked which of 32 promotions their customers responded to. They asked us to segment their customer base so that they could market to each group effectively and appropriately.