Customer clustring with machine learning

Abstract

When analyzing a company that sells in very different product ranges, you are likely to encounter different types of customers. Grouping customers correctly can set standard actions while serving them. Standardization of marketing processes leads to speed and they are easy to improve. While making this classification, the KMeans algorithm was used in Machine Learning. Inertia and Silhouette Points values were used to find the most suitable cluster number. Principal Components Analysis (PCA) was used to show customers with multidimensional features in 2 dimensions.