luthergit / customer_segementation_with_sentence_embeddings

Clustering with Sentence Embeddings

Introduction

There are different ways to go about doing a customer segementation project, here I will be exploring three distinct ways and find the best:

• Kmeans
• K-Prototype
• Sentence Embeddings + Kmeans

This project does not cover EDA or variable selection, which are important steps in this kind of project

Data

The original data used in this project is from a public Kaggle dataset called "Banking Dataset - Marketing Targets" and can be found here

For this project, we will focus on the first 8 columns of the dataset, which include:

• age (numeric)
• job: type of job (categorical)
• marital: marital status (categorical)
• education: education level (categorical)
• default: has credit in default? (binary)
• balance: average yearly balance in euros (numeric)
• housing: has a housing loan? (binary)
• loan: has a personal loan? (binary)

Inside the data folder, you'll find two CSV files: train.csv (the original training dataset) and embedding_train.csv (the dataset after performing an embedding, which will be explained later) using the embedding_creation.py.