AI & machine learning for marketing: a data-driven approach to predict customer satisfaction in air travel industry
Faiola, Stefano (A.A. 2021/2022) AI & machine learning for marketing: a data-driven approach to predict customer satisfaction in air travel industry. Tesi di Laurea in Customer intelligence & big data, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 74. [Master's Degree Thesis]
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Abstract/Index
Customer satisfaction in general. Airlines customer satisfaction. Decision weights and machine learning. US airline passenger satisfaction dataset. Customer ratings. Data viaualisation. Kernel density plot. Scatter plot. Bar chart. Correlation. Data pre-processing. Duplicate values. Missing values. Outliers. Labels encoding. Machine learning model implementation. Logistic regression. Tree-based models. Kernel support vector machine. Feature permutation importance. Shapely additive explanation (SHAP). Results. Academic implications. Managerial implication.
References
Bibliografia: pp. 59-61.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Marketing (LM-77) |
| Chair: | Customer intelligence & big data |
| Thesis Supervisor: | Italiano, Giuseppe Francesco |
| Thesis Co-Supervisor: | Querini, Marco |
| Academic Year: | 2021/2022 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 23 Feb 2023 10:24 |
| Last Modified: | 23 Feb 2023 10:24 |
| URI: | https://tesi.luiss.it/id/eprint/35166 |
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