Optimizing voluntary extra time acceptance prediction: a machine learning solution for Amazon logistic operations
Tresca, Michele (A.A. 2023/2024) Optimizing voluntary extra time acceptance prediction: a machine learning solution for Amazon logistic operations. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 55. [Master's Degree Thesis]
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Abstract/Index
VET flex project introduction. VET model and amazon supply chain. Introduction to the VET model. AWS tools used in the project. Challenges addressed. Amazon supply chain overview. A brief introduction to workforce planning optimization and volume forecasting. Amazon volume forecasting system. Introduction to Amazon forecasting system. Forecasting in the VET flex project context. Forecasting process. Data preprocessing and model optimization. Data preparation and AWS S3 buckets. Data exploration. Data pre-processing and data cleaning. Explorative data analysis and correlation matrix. How we approached the machine learning problem. Binary classification: concepts and metrics. Forecasting models for VET. Classification models. Model output.
References
Bibliografia: pp. 53-54.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Martino, Alessio |
| Thesis Co-Supervisor: | Sinaimeri, Blerina |
| Academic Year: | 2023/2024 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 10 Jul 2025 12:41 |
| Last Modified: | 10 Jul 2025 12:41 |
| URI: | https://tesi.luiss.it/id/eprint/42879 |
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