Rasel, Annajiat AlimMahdee, NafisShourav, Ishrak RahmanTabassum, TasneemNur, EmanMd Amir, Hamza Howlader2022-11-242022-11-2420222022-05http://hdl.handle.net/10361/17617Cataloged from PDF version of thesis.Includes bibliographical references (pages 35-37).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Sales Maximization is a critical aspect of operating any business. Our thesis aims to help businesses to probe deep into their market reach as we group customers us ing the customer segmentation approach. Our dataset is formed based on customer behavior and purchase history. The outcome of this organized study is expected to yield powerful insights in predicting consumer purchasing behavior and related pat terns. Using the K-means algorithm, we analyze real-time transactional and retail datasets. The analyzed data forecasts purchasing patterns and behavior of cus tomers. This study uses the RMF (Recency, Frequency Monetary), LRFM (Length, Recency, Frequency, Monetary), and PCA model deploying K-means on a dataset. The results thus obtained concerning sales transactions are compared with multiple parameters like Sales Recency, Sales Frequency, and Sales Volume.37 PagesID: 18301035ID: 18101664ID: 17101219ID: 17101375ID: 17101528en-USBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.SegmentationCustomer segmentationClusteringK-meansRFMLRFMPCAData miningMachine learningNatural computation--Congresses.Thesis