A k-means cluster model is created on the Heart Disease data, and a centroid table with four clusters is depicted in the submission. A correct and complete explanation of how to interpret the centroid table is written.
Patient data is combined with each individual’s cluster number in a new data frame. An explanation of how this data frame could be used by a data analyst is written.
The four clusters are correctly classified into a low, moderate, high and critical classification with a correct justification for each classification assignment.
One correct patient recommendation is given that is based on data in the k-means analysis.
The relationship between gender and heart disease risk cluster is correctly identified and explained using data in the k-means analysis results.