Machine learning for eco-climatic strategy: predicting air transport emissions
Beltrame, Davide (A.A. 2023/2024) Machine learning for eco-climatic strategy: predicting air transport emissions. Tesi di Laurea in Artificial intelligence and machine learning, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 45. [Bachelor's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Climate change. Overview. Impact of air transport on climate change. A primer on machine learning. Common machine learning algorithms. More on classification and regression. Decision tree algorithms. Air transport emissions and their impact on climate change. Inspiration from Heba Askr et al: study and differences in approach. Tools and libraries used. Data collection and preparation. Data preparation. Feature engineering. Classification prediction. Regression analysis.
References
Bibliografia: pp. 39-41.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Artificial intelligence and machine learning |
| Thesis Supervisor: | Italiano, Giuseppe Francesco |
| Academic Year: | 2023/2024 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 26 Nov 2024 15:01 |
| Last Modified: | 26 Nov 2024 15:01 |
| URI: | https://tesi.luiss.it/id/eprint/40447 |
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