Clustering Custom Data Using the K-Means Algorithm -- Python

The K-Means clustering algorithm is an unsupervised learning algorithm meaning that it has no target labels. It is very tricky to choose the best "K" value. But one way of doing it is the elbow method. According to this method, the sum of squared error (SSE) is calculated for some values of "K". The SSE is the sum of the squared distance between each data point of cluster and its centroid.