Optimizing Marketing Strategy by Performing Customer Segmentation

Customer segmentation plays a pivotal role in enabling businesses to tailor their marketing strategies effectively. This project aims to explore various clustering algorithms including K-means, Agglomerative Hierarchical, DBSCAN, and Gaussian Mixture Models for clustering customer data, considering demographics, purchase history, and behavior. Pre-processing techniques like scaling and dimensionality reduction enhance clustering. K-means minimizes within-cluster distances, yielding distinct clusters for analysis. Agglomerative Hierarchical clustering builds a hierarchy, providing global and local views. DBSCAN identifies clusters based on data density, handling arbitrary shapes and noise. Gaussian Mixture Models capture data distribution with flexible cluster shapes. Evaluation metrics and visualization aid interpretation. The project enables businesses to gain insights and optimize marketing strategies.