| Author | Thatavarthi, Bhavya Sri |
| Note | A research-study submitted in partial fulfillment of the requirements for the degree of
Master of Engineering in
Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Based on Amazon Inc.'s historical 2010-2011 data, Amazon.com new employee access forecast is based on a system designed to replace resource administrators on Amazon. Our analysis shows
that the given dataset with categorical values is very unbalanced. Therefore, during the preprocessing step, we tried different sampling methods, feature selection, and different encoding and frequency coding to make the data more suitable for prediction. In the prediction stage we first tested unique models suitable for vector machines with categorical data support vector machine,
logistic regression, light GBM and neural networks. Finally, we combine the best four prediction results from a random forest, a gradient enhancement and a logistic regression (with encoded data)
and an area under curve (AUC) improvement. |
| Year | 2019 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Guha, Sumanta;Bohez, Erik L. J.;Nicole, Olivier; |
| Scholarship Donor(s) | AIT Fellowship; |