Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rithul, V.; George, Jossy; Nair, Akhil M.; Alapatt, Bosco Paul; Baby, Riya Title A name given to the resource Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Learning and Analytics in Intelligent Systems;Volume;43;pp.77-88 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9855-1_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9855-1_6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006993965?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006993965?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rithul V., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; George J., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Nair A.M., Luxsh Technologies Pvt Ltd., Middlesex, United Kingdom; Alapatt B.P., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Baby R., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India Description An account of the resource Cost optimization in logistics is a very crucial aspect for businesses to remain profitable and competitive by identifying and eliminating unnecessary costs. Most of the researchers concentrated primarily on demand modeling, vehicle routing challenges, and warehouse cost optimization, hence the existing models underperform. This study introduces a novel prediction model that optimizes costs by considering critical factors such as labor charges, material costs, transportation expenses, task types, and branch location. The current model is worked on a primary dataset of 2468 rows and 28 columns which was obtained from an established relocation company in India with all the confidentiality followed. To improve model performance, the required features were adjusted by rigorous feature engineering and data pretreatment techniques such as box-cox scaling, Winsorization, robust scaling, and one-hot encoding. Three ensemble learning techniques were tested: AdaBoost, XGBoost, and gradient boosting. The gradient boosting model correctly captured the complicated nonlinear connections between cost components and income, enabling for cost optimization decisions across a wide range of operational conditions. The proposed model has shown excellent results with the values achieving an MSE of 15% which demonstrates the effectiveness in cost optimization. However, the presence of residuals and potential outliers suggests that more model refinement and process improvements are required. The studys findings offer a data-driven framework for logistics and relocation companies to reduce costs, boost profitability, and gain a competitive advantage in the marketplace. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cost optimization; Logistics; Machine learning; Operational efficiency; Profitability; Relocation Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 26623447; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online