Leveraging machine learning for churn prediction: techniques, challenges and integration with customer lifetime value
Carucci, Lorenzo (A.A. 2023/2024) Leveraging machine learning for churn prediction: techniques, challenges and integration with customer lifetime value. Tesi di Laurea in Data visualization, Luiss Guido Carli, relatore Blerina Sinaimeri, pp. 85. [Master's Degree Thesis]
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Abstract/Index
Literature review. The costumer churn problem. Key terms and definitions. Introduction to churn classification models. Introduction to gradient boosting techniques. LightGBM: an optimized and scalable gradient boosting framework. Unbalanced class problem. SMOTE approach. Data overview and methods. Approach explanation. Input data. Data transformation. Data migration and cloud overview. CAR dataset. Preprocessing and model overview. Data processing steps for churn analysis. Data preprocessing: correlation matrix considerations. Split and SMOTE operations. Algorithms explanation. Result. Tuning. Tuning result. Model implementation. Model inference: application for predictions on new data. Churn probability and targeted retention strategies based on risk levels.
References
Bibliografia. pp. 81-84.
| 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 visualization |
| Thesis Supervisor: | Sinaimeri, Blerina |
| Thesis Co-Supervisor: | Martino, Alessio |
| Academic Year: | 2023/2024 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 15 Apr 2025 11:00 |
| Last Modified: | 15 Apr 2025 11:00 |
| URI: | https://tesi.luiss.it/id/eprint/41812 |
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