CUSTOMER SEGMENTATION WITH K-MEANS CLUSTERING

Abstract

The focus of this project is to investigate and implement the K-Means algorithm, which is a commonly used and well-known clustering method.Clustering is a type of unsupervised learning in which comparable data points are grouped together without the need of labeled data.The K-Means method will be thoroughly investigated, and its implementation will be Accomplished in order to develop a clustering model.The project's major intention is to accomplish consumer segmentation using the K-Means algorithm.The operation of splitting a client base into discrete groups or segments based on shared qualities, habits, or preferences is known as customer segmentation.This can help firms better understand their customers, more effectively focus marketing efforts, and personalize products or services to specific client groups.To calculate the best number of clusters, the project will use the "Elbow method."The Elbow method is a popular K-Means clustering methodology for determining the optimal number of clusters that best represents the underlying structure of the data without overfitting.This project's objective is to investigate and utilize the unsupervised K-Means clustering technique for consumer segmentation.Businesses may get important insights into consumer behavior and preferences by categorizing customers into homogenous groups based on their shared characteristics, allowing them to make data-driven choices and increase overall customer happiness.We will investigate unsupervised learning in this project by applying the K-Means clustering algorithm and employing the Elbow approach to estimate the best number of clusters for customer segmentation.