Mastering Customer Segmentation with LLM

Content Table

1. Intro
2. Method 1: Kmeans

Intro

A customer segmentation project can be approached in multiple ways. In this article I will teach you advanced techniques, not only to define the clusters, but to analyze the result. This post is intended for those data scientists who want to have several tools to address clustering problems and be one step closer to being seniors DS.

What will we see in this article?

Let’s see 3 methods to approach this type of project:

• Kmeans
• K-Prototype
• LLM + Kmeans

Data

The original data used in this project is from a public Kaggle: Banking Dataset — Marketing Targets. Each row in this data set contains information about a company’s customers. Some fields are numerical and others are categorical, we will see that this expands the possible ways to approach the problem.

We will only be left with the first 8 columns. Our dataset looks like this:

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