Building-Customer-Segmentation-Model-using-Python

In this project, we delve deep into the thriving sector of online retail by analyzing a transactional dataset from a UK-based retailer, available at the UCI Machine Learning Repository. This dataset documents all transactions between 2010 and 2011. Our primary objective is to amplify the efficiency of marketing strategies and boost sales through customer segmentation. We aim to transform the transactional data into a customer-centric dataset by creating new features that will facilitate the segmentation of customers into distinct groups using the K-means clustering algorithm. This segmentation will allow us to understand the distinct profiles and preferences of different customer groups. Building upon this, we intend to develop a recommendation system that will suggest top-selling products to customers within each segment who haven't purchased those items yet, ultimately enhancing marketing efficacy and fostering increased sales.