Machine learning in schools #3

Part 1 and Part 2 of the series.

While Ryan was worried about his math test, his teacher was busy sorting test papers. Every year the school would merit students who score above a certain percentage. The school would also devise strategies for students who weren’t performing well. This required them to segregate students into various groups based on their scores. This process of segregating data points with similar properties into groups is what in machine learning is called Clustering.

Clustering is important for businesses since creating strategies for individual customers is not feasible. It is not practically possible for a school to assist every kid at an individual level. However, the school can certainly provide extra coaching to a group of kids who require it.

The feature(s) (in our case test score) on which clusters are formed varies. Amazon might group customers based on their purchasing habits while Netflix might group customers based on genre preferences. Once formed, the clusters are then used to derive insights and drive action. We will see what action Ryan’s school drives from their insights in the next article.

~happy learning.