Elbow function

The Elbow method is a technique used in data analysis to help determine the optimal number of clusters in a dataset when using clustering algorithms like k-means. It involves running the clustering with different numbers of clusters and calculating the total variation or "within-cluster sum of squares" for each. As the number of clusters increases, this variation decreases. The Elbow point is where adding another cluster doesn't significantly improve the fit—look for a bend or "elbow" in the plot of the results. This point suggests the most appropriate number of clusters to balance complexity and accuracy.