Customer Segmentation with K-means Clustering

In this tutorial, we will delve into the implementation of the K-means clustering algorithm to segment customers of a retail store based on their purchase history. We’ll first understand the theory behind K-means clustering and then walk through the code implementation using Python.

1. Introduction to K-means Clustering

K-means clustering is an unsupervised machine learning algorithm used to partition data into K clusters based on similarity. It works by iteratively assigning data points to the nearest cluster centroid and then updating the centroids based on the mean of the data points in each cluster.

2. Dataset Overview

Before diving into code, let’s understand the dataset. We have a dataset containing customer purchase history, where each row represents a customer and each column represents a different product or product category.

To generate a sample dataset customer_purchase_history.csv, we can use Python's pandas library. Below is a code snippet to create a synthetic dataset and save it as 'customer_purchase_history.csv':

import pandas as pd
import numpy as np

# Define the number of customers and products
num_customers = 1000
num_products = 5

# Generate synthetic data
np.random.seed(42)…