Having completed the basics of K-Means clustering in the last 3 weeks, I was tempted to take you through an example problem through code. That is when I realised that we do a lot of pre-modelling activities before we can jump into the model itself. So, today instead of going into K-Means modelling, I thought, why not look at the steps we necessarily (may not be sufficient though) indulge in, before modelling of any sort. Introduction Many want to learn Data Science and Machin

Theoretically and mathematically, we have understood a great deal about K-Means Clustering through Part 1 and Part 2 of this series. If you have not read them, I recommend that you read them prior to going through this blog post. Practical Considerations So, far we have just seen the basics of K-Means algorithms. They certainly help in the unsupervised clustering of data. However, we must realise that not all of this is completely foolproof. There are a lot of decisions and c

Last week, we looked at the basic understanding of how K-Means Clustering works through the 5-step process where the two steps of assignment and optimisation are done iteratively to get stable clusters. For those who have not read it, I would suggest that you read K-Means Clustering: Part 1 of 3 before you proceed further. We know in Machine Learning, we need to understand what is the cost function that any algorithm is trying to work with, so that we can either minimise or m