[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122957-en":3,"doc-seo-122957-105":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},122957,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Predicting Television Programs Success Using Machine Learning Techniques","In the competitive television media landscape, this study leverages machine learning regression models to predict the reach of TV programs with actionable accuracy. The research evaluates model performance and highlights a leading approach achieving a mean absolute percent error under 8%. It also analyzes which input features most strongly influence predictions, then discusses potential improvements achievable by expanding datasets. The findings support TV channel managers in smarter program planning and scheduling, optimizing viewer engagement over time.","Predicting television programs success using machine learning  \ntechniques  \nKhalid El Fayq, Said Tkatek, Lahcen Idouglid  \nComputer Sciences Research Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Aug 15, 2023 Revised Jun 17, 2024 Accepted Jul 2, 2024  \nKeywords:  \nData  \nData science Forecast models Machine learning Predicting Television  \nIn the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models'efficacy, revealing a standout performer with a mean absolute percent error of just under 8% . Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.  \nTelevision program This is an open access article under the CC BY-SA license.  \nCorresponding Author:  \nKhalid El Fayq  \nComputer Sciences Research Laboratory, Faculty of Science, Ibn Tofail University Kenitra, Morocco  \n[Email: khalidelfayq@gmail.com](Email: khalidelfayq@gmail.com)  \n1. INTRODUCTION  \nThe marketing industry stands as one of the largest in the world. Television (TV) companies invest millions of dollars in marketing, and audience ratings can assist advertisers in obtaining the information they need to create ads that meet their audience's needs. This information also provides content creators and TV networks with the ability to make improvements and plays a crucial role in various areas, including content promotion and programming.  \nIn Morocco, the audience measurement system stands at the forefront of innovation. The current audience measurement methods in Morocco operate on the principle of sampling. Several key organizations play pivotal roles in this process, including the High Authority for Audiovisual Communication (HACA), which is the regulatory body overseeing audiovisual communication, the Interprofessional Media Audience Center (CIAUMED), responsible for establishing and implementing the media measurement system for audiences in Morocco. Additionally, Marocmétrie is entrusted with the crucial responsibility of collecting and processing media audience data in Morocco, and the national radio and television company (SNRT) is actively engaged, overseeing the measurement of audiences for all Moroccan channels across the entire Moroccan territory, encompassing both urban and rural demographics.  \nTo contextualize this study within the broader field of television programming and audience measurement, it is imperative to acknowledge the state-of-the-art. Existing studies, exemplified by [1]–[7], predominantly focus on predicting impressions for specific content, movies, series, and TV ratings. However,  \na discernible gap emerges in the arena of predicting the overall reach of a diverse audience. This paper endeavors to address this gap by employing predictive analytics, specifically aiming to enhance the overall TV audience percentage. Drawing insights from a specialized company in Moroccan television audience statistics, we seek to contribute novel perspectives to the existing body of knowledge.  \nLaayoune channel, one of the Moroccan television channels, has its daily TV guides manually sche","cbCain2iFhPw3wm6","https://ap.wps.com/l/cbCain2iFhPw3wm6","pdf",970744,1,11,"English","en",105,"# Introduction\n## Audience measurement context in Morocco\n## Research gap and study objective\n## Motivation from manual TV scheduling errors\n## Related work in TV ratings prediction","[{\"question\":\"What is the main goal of the study on TV programs?\",\"answer\":\"To use machine learning regression models to predict the reach of TV programs accurately and evaluate how effective these models are.\"},{\"question\":\"How well do the models perform, according to the abstract?\",\"answer\":\"A standout performer reaches a mean absolute percent error just under 8%.\"},{\"question\":\"Why do feature selection and dataset expansion matter in this research?\",\"answer\":\"The study identifies the features with substantial impact on predictions and suggests that enlarging datasets could improve model performance.\"}]","Predicting Television Programs Success Using Machine Learning Techniques | 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