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 Conference Paper Faculty Publications- Conference Papers 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 Patel, Dharma; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Aruna, S.K.; Vinodha, D. Title A name given to the resource Leveraging ML Based Technique for Mobile Sales Forecasting 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 Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.23919/INDIACom66777.2025.11115351" target="_blank" rel="noreferrer noopener">https://doi.org/10.23919/INDIACom66777.2025.11115351</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016114906?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016114906?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 Patel D., Soet, Christ University, Dept. of Cse, Bangalore, India; Sambandam R.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vetriveeran D., Soet, Christ University, Dept. of Cse, Bangalore, India; Jenefa J., Soet, Christ University, Dept. of Cse, Bangalore, India; Aruna S.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vinodha D., Soet, Christ University, Dept. of Cse, Bangalore, India Description An account of the resource The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi. Subject The topic of the resource Artificial Intelligence; Decision Tree; Forecasting; Machine Learning; Mobile Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 978-938054460-1; Language A language of the resource English Type The nature or genre of the resource Conference paper 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