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 Conference Papers 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/. Title A name given to the resource Machine Learning based Food Sales Prediction using Random Forest Regression Subject The topic of the resource Artificial Intelligence; Food; Forecasting; Machine Learning; Regression; Sales prediction Description An account of the resource Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE. Creator An entity primarily responsible for making the resource Naik H.; Yashwanth K.; Suraj P.; Jayapandian N. Source A related resource from which the described resource is derived 6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022 - Proceedings, pp. 998-1004. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA55336.2022.10009277" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA55336.2022.10009277</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147508339&amp;doi=10.1109%2FICECA55336.2022.10009277&amp;partnerID=40&amp;md5=1edc820e13d33dfa59ab3cf1f4e4b76a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147508339&amp;doi=10.1109%2fICECA55336.2022.10009277&amp;partnerID=40&amp;md5=1edc820e13d33dfa59ab3cf1f4e4b76a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166548271-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Naik H., Christ University, Department of Cse, India; Yashwanth K., Christ University, Department of Cse, India; Suraj P., Christ University, Department of Cse, India; Jayapandian N., Christ University, Department of Cse, India