Feature Engineering for Modern Machine Learning with Scikit-Learn

1. Understanding the K-means Clustering Algorithm

K-means clustering is an unsupervised learning technique used to divide data points into K distinct clusters. This algorithm is particularly effective for customer segmentation due to its ability to group similar customers based on shared characteristics. Here's a more detailed look at how K-means works and why it's valuable for market analysis:

Cluster Assignment: Each data point is assigned to the cluster with the nearest centroid. This process involves calculating the Euclidean distance between the data point and each cluster's centroid, then associating the point with the closest one.

Centroid Recalculation: After assigning all points, the algorithm recalculates the centroids of each cluster ...