Segmentation of small and medium sized enterprises (SMEs) into clusters for better insights and market targeting.

How can a bank serve its customers better? How can it better position itself to provide loans to the small and medium sized enterprises (SMEs) to help them in their business?

Given 3 sets of data - SME bank accounts information, bank transactions in 2018, and bank transactions in 2019, let's segment the SME market into different clusters to gain insights into their individual characteristics and behaviors.

In this study, the usual data science process is followed to gain insights into the available data. The k-means algorithm is used to cluster the existing 112,618 customer accounts into 6 clusters. Insights are gained from each clusters.