| Title |
Stone, Jacob MCS_2024 |
| Alternative Title |
An Analysis of Unsupervised, Semi-Supervised, and Supervised Machine Learning Models to Categorize Procurement Data |
| Creator |
Stone, Jacob |
| Collection Name |
Master of Computer Science |
| Description |
This thesis focuses on both aspects of manually categorizing items and using machine learning to analyze the results of both. The former holds 100% accuracy with a huge time cost, and the latter has varying performances, with some seemingly being beneficial and some not. |
| Abstract |
Categorizing item purchases can be a headache for major companies, but it can be used in beneficial practices such a cost tracking and other useful metrics of spending. The major issue that can hold a company back is the time cost of categorizing every item that is purchased. Once the bulk of this work is done, however, machine learning can be done to further classify more items without the same time spent. This thesis focuses on both aspects of manually categorizing items and using machine learning to analyze the results of both. The former holds 100% accuracy with a huge time cost, and the latter has varying performances, with some seemingly being beneficial and some not.; Six different machine learning algorithms were implemented spanning from unsupervised, semi-supervised, and supervised learning models. There are two different methods of balancing the datasets and hours spent to determine the optimal preprocessing steps. Each model was then tuned to the best hyperparameters to find the best performance, and the time to execute is used as an evaluation criterion. |
| Subject |
Machine learning; Artificial intelligence; Algorithms |
| Digital Publisher |
Stewart Library, Weber State University, Ogden, Utah, United States of America |
| Date |
2024 |
| Medium |
Thesis |
| Type |
Text |
| Access Extent |
959 KB; 47 page pdf |
| Rights |
The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce his or her theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights. |
| Source |
University Archives Electronic Records: Master of Education. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s6401bp2 |
| Setname |
wsu_smt |
| ID |
129706 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s6401bp2 |
