[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290749-105":59,"doc-detail-290749-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","food-sales-prediction-model-using-machine-learning-techniques-best-algorithms-comparison","Food Sales Prediction Model using Machine Learning Techniques - Best Algorithms Comparison","","Food sales prediction focuses on forecasting future company sales quantities to reduce food spoilage from expiration and avoid overstocking that leads to warehousing losses. The study proposes a machine learning–based prediction model with two objectives: comparing two datasets with high versus low feature correlation, and evaluating multiple learning algorithms to identify the best three predictors. Performance is judged using metrics such as RMSE and MSE, selecting top models separately for each dataset.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/food-sales-prediction-model-using-machine-learning-techniques-best-algorithms-comparison/290749/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/food-sales-prediction-model-using-machine-learning-techniques-best-algorithms-comparison/290749.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the food sales prediction model aim to solve?","Question",{"text":112,"@type":113},"It forecasts future sales quantities to prevent food spoilage due to expiration and to avoid buying more than needed, which otherwise causes products to accumulate in warehouses.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are the two main objectives of the study?",{"text":117,"@type":113},"First, compare two datasets with high vs. low feature correlation. Second, test several machine learning algorithms and select the best three based on predictive performance.",{"name":119,"@type":110,"acceptedAnswer":120},"Which metrics were used to evaluate and choose the best algorithms?",{"text":121,"@type":113},"The study uses metrics such as root mean square error (RMSE) and mean square error (MSE) to compare models and identify the top-performing algorithms for each dataset.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290749,1790141957,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","Food sales prediction model using machine learning techniques  \nHussam Mezher Merdas, Ayad Hameed Mousa  \nDepartment of Computer Science, College of Computer Science and Information Technology, University ofKerbala, Karbala, Iraq  \nArticle history:  \nReceived Jan 9, 2023 Revised Mar 15, 2023 Accepted Apr 7, 2023  \nKeywords:  \nBagging regressor Decision tree  \nFood sales prediction Gradient boosting  \nLeast absolute shrinkage and selection operator  \nRandom forest regressor Support vector machines  \nCorresponding Author:  \nFood sales prediction means how to obtain future results of sales of companies. The purpose of this step is to increase the profits of these companies by avoiding spoilage of food products and avoiding buying more quantities than the needs of these companies, which means the accumulation of these products in the warehouses without selling them. Stocked and expired products require a model that guesses the actual future need for these products. In this study, a model for food sales prediction using machine learning algorithms is proposed to achieve two objectives, first: make a comparison between two datasets, one dataset with a high correlation between its features, and another dataset has a low correlation between its features. The second objective is to use several machine learning algorithms for prediction and comparing between these algorithms to find the best three algorithms that give the best prediction. By using the most important metrics such as root mean square error (RMSE) and mean square error (MSE) found the best three algorithms by using the first dataset are support vector machines (SVMs), least absolute shrinkage and selection operator (LASSO), and bagging regressor) and the best three algorithms by using the second dataset are (gradient boosting, random forest regressor, and decision tree) .  \nThis is an open access article under the CC BY-SA license.  \nHussam Mezher Merdas  \nDepartment of Computer Science, College of Computer Science and Information Technology, University ofKerbala  \nKarbala, Iraq  \n[Email: hussam.m@s.uokerbala.edu.iq](Email: hussam.m@s.uokerbala.edu.iq)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nFood sales are an inspiring topic for researchers today due to their importance for companies, merchants, and entrepreneurs [1] . However, food sales prediction is an important and critical step and at the same time useful for these companies to know the expected quantities of food will need in the future and ensure that food is not damaged as a result of its expiration date [2] . Accurate prediction of food sales is a difficult task due to the dynamic change of external parameters. In general Time series data control food sales prediction [1] . The huge amounts of data collected and the large number of features affect each other making data impossible to be processed manually. Here comes the role of artificial intelligence to solve this problem and automatically predict the future of sales. It is the responsibility of artificial intelligence algorithms to process the data and give the appropriate prediction. In this regard, many algorithms differ from each other in the results they provide to give a good prediction.  \nMany researchers have addressed the topic of food sales prediction, and many algorithms have been used. But most researchers did not use only one, two, or three algorithms. Also, they often use one dataset. As previously mentioned, this study, used more than one dataset and more than one algorithm. So, the related works can be listed below. According to the study mentioned in source [3], the researchers designed two prediction models: the main prediction model and the weather prediction model to prove the effect of weather  \nfactors on the sales prediction of fresh products. The algorithms used are random forest (RF), ridge regression (RR), and support vector machine (SVM) . The results of this study by using the relative reduction rate of the using root mean square ","cbCaiksaSOGd8Zxj","https://ap.wps.com/l/cbCaiksaSOGd8Zxj","pdf",471051,"English","# Abstract\n## Introduction\n## Related Work","[{\"question\":\"What problem does the food sales prediction model aim to solve?\",\"answer\":\"It forecasts future sales quantities to prevent food spoilage due to expiration and to avoid buying more than needed, which otherwise causes products to accumulate in warehouses.\"},{\"question\":\"What are the two main objectives of the study?\",\"answer\":\"First, compare two datasets with high vs. low feature correlation. Second, test several machine learning algorithms and select the best three based on predictive performance.\"},{\"question\":\"Which metrics were used to evaluate and choose the best algorithms?\",\"answer\":\"The study uses metrics such as root mean square error (RMSE) and mean square error (MSE) to compare models and identify the top-performing algorithms for each dataset.\"}]","Food Sales Prediction Model using Machine Learning Techniques - Best Algorithms Comparison | PDF",1789642500]