Understanding Customer Segmentation with DBSCAN Clustering in Python

Customer segmentation is a vital aspect of marketing and business strategy. It involves categorizing customers into groups based on shared characteristics such as purchase history, demographics, behavior, and more. These segments help businesses tailor their marketing efforts and provide better services to each group, ultimately improving customer satisfaction and profitability.

In this blog post, we will explore how to perform customer segmentation using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm in Python. We’ll use a customer dataset from Kaggle to demonstrate the process.

What is DBSCAN?

DBSCAN is a density-based clustering algorithm that groups together data points that are close to each other in the feature space. Unlike k-means, which assumes clusters of similar sizes and shapes, DBSCAN can discover clusters of arbitrary shapes and sizes. It’s particularly useful when dealing with data where clusters are not well-defined or have varying densities.

Getting Started

Before we dive into the code, make sure you have Python installed on your machine along with the necessary libraries, including numpy, pandas, matplotlib, and scikit-learn. You can install them using pip if you haven't already:

pip install numpy pandas matplotlib scikit-learn