03707ntm a22005177i 4500 000264295 CZ-PrVSE 20220907191439.0 m d cr n|||||||||| 220907s2022 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Kalashnikova, Liudmila ISIS:158075 dis Next Best Action : Review and Case Study eng Next Best Action : Review and Case Study Liudmila Kalashnikova 2022 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Kliegr Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2022 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2022 The development of information technologies and data analysis in particular transformed significantly the processes of decision-making. Companies and institutions strive to make data-driven decisions, and this interest continues to grow. As a result, the field of recommender systems requires new research; therefore, the study of the Next Best Action approach and the features of its application in practice is a task of current interest. The main purpose of the work is to explore the current state of the Next Best Action approach in the data science community by the example of the Covid-19 Government Response Tracker. In the first theoretical part of the work, a review of NBA concept and an analysis of publications on the topic are presented. The chapter includes a justification of the relevance of the topic. In the second part of the work, data description for case study, primary data analysis, application of Next Best Action via Action Rules and Reinforcement Learning and evaluation of the results are proposed. The third part of the work covers development of the Flask-based web-interface. The final outcome of the work is the interactive web-application that allows determining the next best action for preventing Covid-19, the outcome of the interface is based on the initial parameters set by a user and it uses the NBA models, which correspond to current trends in the field. The obtained results of the work could be applied to optimize their activities by data scientists; moreover, case study results might potentially provide a useful tool for government institutions to exchange experience in fighting Covid-19 between countries and support decision-making in this area. Způsob přístupu: Internet information systems management [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Next Best Action Machine Learning Action Rules Reinforcement Learning Kliegr, Tomáš ISIS:8484 ths Sýkora, Lukáš ISIS:36976 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/78513/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/78513 Hlavní práce https://insis.vse.cz/zp/78513/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/78513/posudek/oponent/75972 Oponentura https://insis.vse.cz/zp/78513/priloha/24673 Přiloha k práci https://insis.vse.cz/zp/78513/podrobnosti dc:identifier NEPOSILAT VSKP vse78513 220826 78513