Clustering is a method of grouping records in a database based on certain criteria. One method of clustering is K-Means Clustering. K-Means Clustering divides data into multiple data sets and can accept data inputs without class labels. This research uses the K-Means Clustering method and implements it on the Heart disease dataset. In this paper, the risk factors that cause heart disease are considered and predicted using the K-means algorithm and the analysis is carried out using publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate, and four types of chest pain. To predict heart disease, a K-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances, and metrics. In the result section, the visualized data shows that the prediction is accurate.