Chakrabarty, AmitabhaNabil, Sadman SakibIslam, Md TanvirMuhit, Sadman Aziz2025-02-182025-02-1820242024-10ID 19101501ID 20101379ID 19201070http://hdl.handle.net/10361/25439Cataloged from PDF version of project report.Includes bibliographical references (pages 39-41).This project report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.In today’s aggressive and fast-paced economy, the ability to forecast sales accurately and effectively denotes a proper utilization of the available resources in planning. Typical sales forecasting methods fail quite often to measure the dynamic market environment owing to the fact that they are totally influenced by past data and also expert opinion. Therefore, this research seeks to validation of sales forecast accuracy with respect to the integration of machine learning (ML) in enhancing its capability. Considering available historical sales figures and some social media trends, machine learning techniques are able to provide realistic and satisfactory forecasts. The paper discusses the advantages of machine learning (ML) to the old methods, for instance, quick detection of the emerging trends, dealing with big data, and adaptation to the situation. Some problems, such as data quality and system integration are also considered. Some of these include ensemble methods, neural networks, and regression, and such techniques are used in machine learning. This article discusses how the integration of machine learning (ML) in sales forecasting will help companies in management and decision making leading to better performance compared to competitors.51 pagesenBRAC University project reports 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.Sales forecastingMachine learningLinear regressorRandom forest regressorDecision treeRegression dataARIMALasso regressionData mining.Business forecasting.Deep learning (Machine learning).Sales--Forecasting.Project Report