[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122761-en":3,"doc-seo-122761-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122761,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Menu Recommendation system using Machine Learning - Capstone Project Report","Developing a recommendation menu system for restaurants using restaurant data and city food purchase data to change how menus are built and to increase customer ordering, repeat visits, and recommendations. Data analysis and machine learning support more accurate ingredient and dish suggestions by predicting which ingredients customers are likely to want. The work covers prediction tools, project planning, dataset collection, data manipulation, and evaluation, with the core business goal of recommending next-menu ingredients to improve satisfaction and address food waste and related inequalities.","CCT College Dublin  \nARC (Academic Research Collection)  \nICT  \n2023  \nMenu Recommendation system using Machine Learning Kelly Crystine Ferreira Jesus  \nCCT College Dublin  \nLeo Jaime Kayser Macieski CCT College Dublin  \nFollow this and additional works at: [https://arc.cct.ie/ict](https://arc.cct.ie/ict)  \n Part of the Computer Sciences Commons  \nRecommended Citation  \nFerreira Jesus, Kelly Crystine and Kayser Macieski, Leo Jaime, \"Menu Recommendation system using Machine Learning\" (2023) . ICT. 38.  \n[https://arc.cct.ie/ict/38](https://arc.cct.ie/ict/38)  \nThis Capstone Project is brought to you for free and open access by ARC (Academic Research Collection) . It has been accepted for inclusion in ICT by an authorized administrator of ARC (Academic Research Collection) . For more information, please [contact debora@cct.ie](contact debora@cct.ie).  \nMenu Assistance  \nKelly Crystine Ferreira Jesus-2019375 Leo Jaime Kayser Macieski-2019221  \nA Report Submitted in Partial Fulfilment of the requirements for the Degree of  \nBSc in Computing in IT (4th year)  \nMay 2023  \nSupervisor: Dr. Muhammad Iqbal  \nAbstract  \nDeveloping a recommendation menu system for restaurants based on the restaurant data and/or city food purchase data to help and change the way restaurants build their menu.  \nUsing Data Analysis and Machine Learning to build a project that aims to solve the problem of restaurants and chefs when it comes to preparing menus, the latter with ingredients and dishes that encourage their customers to order more, come back and recommend the restaurant. Helping chefs to create dishes for their restaurants with more accuracy and higher probability to be ordered by their customers.  \nThe project will cover tools to build the predictions, the project plan, collect datasets, manipulate data and evaluate the aspects of the situation. The main business goal of our project is to predict what ingredients customers would like to eat and, from that, give restaurants ingredient suggestions to create their next menu.  \nBy providing an efficient ingredients decision maker, it will simplify the way menus are elaborated and improve overall customers satisfaction.  \nAnother motivating factor in choosing this project was its potential to help society tackle the huge problem of food waste and the inequalities this entails.  \nAcknowledgements  \nOur appreciation for the lecturers' support, Ken Healy, David McQuaid and Aldana Louzan. The project supervisor Dr. Muhammad Iqbal, for their patience and feedback, which their classes helped in building our project structure.  \nWe also could not have undertaken the completion without our classmates, who generously provided exchange of knowledge during the classes.  \nTable of Contents  \nAbstract 3  \nAcknowledgements 4  \nIntroduction 6  \nBriefly about the project Methodology 7  \nObjectives to achieve the project goals 8  \nRoles and Responsibilities 9  \n1. Business Understanding-1st Phase 11  \n1.1. Project plan 11  \n1.2. Project resources 12  \n1.3. Requirements, assumptions and constraints 12  \n1.4. Costs and benefits 13  \n1.5. Data mining/Machine Learning goals 13  \n1.6. Summary 14  \n2. Data Understanding-2nd Phase 15  \n2.1. Collecting and Describing data 15  \n2.2. Exploratory Data Analysis (EDA) 18  \n2.3. Verifying data Quality 20  \n2.4. Summary 21  \n3. Data Preparation -3rd Phase 22  \n3.1. Selecting Data 22  \n3.2. Data Cleaning 22  \n3.3. Data Construction 24  \n3.4. Data Integration 27  \n3.5. Summary 31  \n4. Modelling-4th Phase 32  \n4.1. Models/Machine Learning (ML) 32  \n4.2. Splitting, Training and Testing models 34  \n4.3. Model Evaluation 38  \n4.4. Summary 40  \n5. Evaluation-5th Phase 41  \n5.1. Evaluate results and Review process 41  \n5.2. Summary 44  \n6. Deployment-6th Phase 45  \n6.1. Strategy and Steps 45  \n6.2. Monitoring and Maintenance 46  \n6.3. Final Thoughts 48  \n6.4. Summary 49  \nConclusion 50  \nAppendix 51  \nCitations and references 52  \nIntroduction  \nDeveloping a recommendation menu system for re","cbCaihvIH9TnBvBp","https://ap.wps.com/l/cbCaihvIH9TnBvBp","pdf",10297566,1,53,"English","en",105,"# Introduction\n## Project goal and problem statement\n## Dashboard and menu recommendation scope\n# Methodology Overview\n## Business Understanding - 1st Phase\n## Data Understanding - 2nd Phase\n## Data Preparation - 3rd Phase\n## Modelling - 4th Phase\n## Evaluation - 5th Phase\n## Deployment - 6th Phase\n# Conclusion\n# Appendix\n# Citations and references","[{\"question\":\"What does the menu recommendation system predict?\",\"answer\":\"It predicts ingredients customers would like to eat, then uses those predictions to suggest ingredients for the next restaurant menu.\"},{\"question\":\"Which data sources does the project rely on?\",\"answer\":\"The system uses restaurant data and/or city food purchase data, with additional local grocery data suggested for the first-time menu.\"},{\"question\":\"How does the project help restaurants and chefs?\",\"answer\":\"By simplifying menu creation with an efficient ingredients decision maker, it supports more accurate dish and ingredient planning and aims to improve customer satisfaction while reducing food waste.\"}]","Menu Recommendation system using Machine Learning - 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