Pre-requisites: Install the free Anaconda for Python 3.6. Procedure: 1º Start with a table of data in a excel worksheet. The row will be what you want to cluster, in the end this program creates a new ...

The major weakness of k-means clustering is that it only works well with numeric data because a distance metric must be computed. There are a few advanced clustering techniques that can deal with ...

In this project, I implement K-Means clustering with Python and Scikit-Learn. K-Means clustering is used to find intrinsic groups within the unlabelled dataset and draw inferences from them. I have ...

K-NN is a very basic supervised learning ML algorithm. It is non-parametric: don’t make any assumption about the distribution of the data), Also we called it lazy learner, it means that it doesn ...

By using K-Means clustering, an online retailer may identify that its client base naturally divides into three groups: budget-conscious shoppers, regular shoppers, and luxury shoppers.