K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. Sliders to set K-value and Data points and then click the green flag to start. Algorithm: Step-1: Select the number K to decide the number of clusters. Step-2: Select random K points or centroids. (It can be other from the input dataset). Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters. Step-4: Calculate the variance and place a new centroid of each cluster. tep-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster. Step-6: If any reassignment occurs, then go to step-4 else go to FINISH. Step-7: The model is ready.
Project Reference: https://en.wikipedia.org/wiki/K-means_clustering https://scratch.mit.edu/projects/190300489/ Thank you cactus3
