University of Twente Student Theses
Predicting product characteristics using neural networks
Umamahesh Ritty, Nikitha (2023) Predicting product characteristics using neural networks.
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| Abstract: | As the significance of product quality and the demand for efficient and precise predictions increases, machine learning techniques have become a desirable solution for businesses. However, it can be difficult to determine the most suitable technique for a particular task. Prior research has highlighted the effectiveness of machine learning techniques in prediction tasks, prompting this research to focus on developing a predictive model using a neural network approach and exploring different approaches within the neural network. This research specifically investigates techniques related to data preprocessing and feature selection, with the aim of identifying effective approaches to optimize the performance of neural networks. The study evaluates various neural network architectures and associated criteria to determine the best configuration for accurate predictions. By systematically comparing these methodologies, it provides insights into their strengths and limitations, aiding decision-making in implementing neural networks for predicting product quality. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business Information Technology MSc (60025) |
| Link to this item: | https://purl.utwente.nl/essays/97314 |
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