• Developed a customer segmentation strategy tailored for retail operations; successfully identified high-value customer segments, resulting in a 15% boost in average order value and a 10% increase in customer retention rate. • Leveraged Principal Component Analysis (PCA) to reduce data dimensionality, selecting two principal components
that explained 85% of the variance. Resulted in a 40% improvement in model efficiency. • Implemented the Gaussian Mixture Model algorithm to facilitate clustering, achieving an optimal silhouette score
of approximately 0.422 for two distinct clusters.