Information system for forecasting sales of the range of building materials on the online trading platform based on the neural network tensor flow
creativework.keywords | Information system, sales forecasting, neural network Tensor flow | |
dc.contributor.author | Semkiv Mykhaylo | |
dc.contributor.author | Vysotska Victoria | |
dc.contributor.author | Shakleina Iryna | |
dc.date.accessioned | 2022-10-21T09:19:13Z | |
dc.date.available | 2022-10-21T09:19:13Z | |
dc.date.issued | 2022 | |
dc.description.abstract | This work is devoted to creating a system for forecasting sales of building materials numbers on the online trading platform based on the neural network Tensor flow. It was decided to develop an information-analytical system by building a tree of goals and analysing the hierarchy. The system is created as an application that runs in a web browser. The application will be available to everyone with the ability to add restrictions later. Sales forecasting is done by obtaining data from various APIs and working with ready-made data sets, allowing you to forecast the future product offer dates. To get a sales forecast, you need to enter data, train the model, validate our finished model and make a forecast. | |
dc.identifier.citation | Semkiv M. Information system for forecasting sales of the range of building materials on the online trading platform based on the neural network tensor flow / Mykhaylo Semkiv, Victoria Vysotska, Iryna Shakleina // Computational Linguistics and Intelligent Systems. – Lviv, 2022. – Volume 2 : Proceedings of the 6nd International conference, COLINS 2022. Workshop, Gliwice, Poland, May 12–13, 2022. – P. 229–254. – URL: https://colins.in.ua/wp-content/uploads/2022/07/VolumeII_Colins2022.pdf (дата звернення: 21.10.2022). – Bibliography: 32 titles. | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/56979 | |
dc.language.iso | en | |
dc.publisher | онлайн | |
dc.title | Information system for forecasting sales of the range of building materials on the online trading platform based on the neural network tensor flow | |
dc.type | Article |