[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121783-en":3,"doc-seo-121783-105":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},121783,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Forecasting Demand in the Pharmaceutical Industry Using Machine Learning - Internship Report","This study applies three machine learning approaches—XGBoost, LSTM, and the Prophet algorithm—to improve demand forecasting in the pharmaceutical industry. Using the CRISP-DM framework, historical sales records from a major Portuguese pharmaceutical company were studied, cleaned, transformed, and used for model training and evaluation. Results support the literature by showing XGBoost’s robustness, the LSTM’s limited usefulness for this task, and Prophet’s effectiveness and efficiency. Findings emphasize drug/product-specific forecasting rather than a single universal model.","MDDM  \nMaster’s Degree Program in  \nData-Driven Marketing  \nForecasting Demand in the Pharmaceutical Industry Using Machine  \nLearning  \nJoão Aires Lancastre de Sousa Cabral de Ascensão  \nInternship Report  \npresented as a partial requirement for obtaining the master’s degree Program in Data-Driven Marketing with a  \nspecialization in Data Science for Marketing  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nFORECASTING DEMAND IN THE PHARMACEUTICAL INDUSTRY  \nUSING MACHINE LEARNING  \npor  \nJoão Aires Lancastre de Sousa Cabral de Ascensão  \nProjeto apresentado como requisito parcial para obtenção do grau de Mestre em Data-Driven Marketing, com especialização em Data Science for Marketing  \nOrientador: Rui Alexandre Henriques Gonçalves  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nLisbon, 15th of July 2023  \nABSTRACT  \nThis study delves into the exploitation of three machine learning models, namely the Extreme Gradient Boosting (XGBoost), the Long Short-Term Memory (LSTM), and the novel Prophet algorithm, to surpass the challenge of demand forecast within the pharmaceutical industry. Following the CRISP-DM framework, we enabled accurate sales forecasting by studying, treating, transforming, and training adataset containing historical sales data from a major Portuguese pharmaceutical company. Our findings align with the literature, underlying the robustness of the XGBoost and the inefficacy of the LSTM for the delineated task, considering the singularities of the provided data. Furthermore, this research highlights the potential of the Prophet for both its effectiveness and efficiency. This endeavor allowed us to reinforce the literature’s conviction of the need for product-specific forecasting, showcasing that no single model achieves the best accuracy for all drugs.  \nKEYWORDS  \nDemand Forecasting; Pharmaceutical Sales; Machine Learning.  \nSustainable Development Goals (SGD):  \nINDEX  \n1. Introduction................................................................................................................... 1  \n1.1 Internship Context ................................................................................................. 1  \n1.2 Study Relevance and Importance..........................................................................1  \n1.3 Problem and Main Goals .......................................................................................2  \n2. Literature review ...........................................................................................................3  \n2.1 Forecasting Demand in the Pharmaceutical Industry ............................................3  \n2.2 Selected Models .....................................................................................................6  \n3. Methodology .................................................................................................................7  \n3.1 CRISP-DM Framework ...........................................................................................7  \n3.2 Demand Forecasting Model ...................................................................................8  \n3.1.1 Business Understanding .................................................................................8  \n3.1.2 Data Understanding and Preparation ............................................................9  \n3.1.3 Modeling and Evaluation................................................................................9  \n4. Results .........................................................................................................","cbCairJA50LBZrPI","https://ap.wps.com/l/cbCairJA50LBZrPI","pdf",861096,1,32,"English","en",105,"# Introduction\n## Internship Context\n## Study Relevance and Importance\n## Problem and Main Goals\n# Literature review\n## Forecasting Demand in the Pharmaceutical Industry\n## Selected Models\n# Methodology\n## CRISP-DM Framework\n## Demand Forecasting Model\n## Business Understanding\n## Data Understanding and Preparation\n## Modeling and Evaluation\n# Results\n# Discussion\n# Conclusions and future works","[{\"question\":\"Which machine learning models are used for the demand forecasting task?\",\"answer\":\"The report evaluates XGBoost, LSTM, and the Prophet algorithm for forecasting pharmaceutical demand.\"},{\"question\":\"How is the forecasting work structured methodologically?\",\"answer\":\"It follows the CRISP-DM framework, covering business understanding, data understanding and preparation, and modeling and evaluation.\"},{\"question\":\"What do the results indicate about model performance across drugs?\",\"answer\":\"The findings show XGBoost is robust, LSTM is ineffective for the defined task with the provided data characteristics, and Prophet offers both effectiveness and efficiency. The study also concludes that no single model achieves the best accuracy for all drugs, supporting product-specific forecasting.\"}]","Forecasting Demand in the Pharmaceutical Industry Using Machine Learning - Internship Report | PDF",1785806814,81,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-demand-in-the-pharmaceutical-industry-using-machine-learning-internship-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-demand-in-the-pharmaceutical-industry-using-machine-learning-internship-report/121783/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used for the demand forecasting task?","Question",{"text":75,"@type":76},"The report evaluates XGBoost, LSTM, and the Prophet algorithm for forecasting pharmaceutical demand.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the forecasting work structured methodologically?",{"text":80,"@type":76},"It follows the CRISP-DM framework, covering business understanding, data understanding and preparation, and modeling and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about model performance across drugs?",{"text":84,"@type":76},"The findings show XGBoost is robust, LSTM is ineffective for the defined task with the provided data characteristics, and Prophet offers both effectiveness and efficiency. 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