Jahan, Sifat EMostakim, MoinMuhtashima, FawziaMaksurah, Fawzia2024-04-242024-04-2420232023-05ID 19101204http://hdl.handle.net/10361/22664Cataloged from PDF version of thesis.Includes bibliographical references (pages 20-21).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023."This paper focuses on addressing some challenges faced by colorists and explores various approaches to predict fabric color changes after dyeing processes. It empha- sizes the importance of color prediction in the textile industry and proposes suitable models that can effectively carry out color prediction tasks based on given recipes. By implementing such predictive models, the textile industry can improve efficiency, reduce labor-intensive practices, and enhance the overall quality control process. The methods used in this study are supervised machine learning techniques, in- cluding multiple linear regression, decision tree, random forest, and neural network. Among these models, the most appropriate one is selected and further optimized using feature engineering techniques to improve accuracy"21 pagesenBrac 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.Color predictionDecision treeLinear regressionNeural networkFeature EngineeringMachine learningNeural networks (Computer science)Thesis