The Elbow Method for K-Means Clustering in Python Template
The Elbow Method for K-Means Clustering in Python template demonstrates a way to determine the most optimal value of K in a K-Means clustering problem. Recall that K represents the numbers of clusters. The way this is done is through the so-called elbow method which requires calculating the within-cluster sum of squares for each number of clusters.. Some other related topics you might be interested in are K-Means Clustering of Numerical Data with sklearn in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python. You can now download the Python template for free. The Elbow Method for K-Means Clustering in Python template is among the topics covered in detail in the 365 Data Science program.
Who is it for
This is an open-access Python template that is going to be very helpful for Data Analysts, Machine Learning Engineers, Data Scientists and anyone who wants to familiarize themselves with the K-Means elbow method in the Python programming environment.
How it can help you
Clustering is a type of unsupervised machine learning algorithm. What this means is that the data points in a dataset are not accompanied by a target value. Instead, once clusters have been constructed, it is the user who has to interpret the results. This template can be used whenever the user needs to decide on the most optimal value for the number of clusters.