k means clustering
In today's world, data plays an integral role in driving business decisions. One of the most common and effective ways of analyzing data is through machine learning algorithms. K-Means clustering is an unsupervised machine learning algorithm that has gained popularity for its efficiency in data clustering. What is K-Means Clustering? K-Means clustering is an algorithm used to create clusters based on the data fed to the machine. The algorithm works by randomly assigning a color to a few data points, known as the centroid kids, and grouping the surrounding data points based on the mean distance from each centroid. Applying K-Means Clustering to Customer Segmentation For instance, retailers can use K-Means clustering to decide which customer gets promotional offers. They can create three clusters of customers, namely the loyal, somewhat loyal, and lowest priced shoppers, based on their shopping patterns. Then, they can create strategies to convert somewhat loyal customers into loyal