Predictive modeling for identifying success factors in start-ups: an empirical analysis
Violides, Marc (A.A. 2022/2023) Predictive modeling for identifying success factors in start-ups: an empirical analysis. Tesi di Laurea in Data analysis for business, Luiss Guido Carli, relatore Francesco Iafrate, pp. 85. [Bachelor's Degree Thesis]
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Abstract/Index
Literature review. Overview of start-ups: definition, challenges and success factors. The role of machine learning in the success of start-ups. Methodology. Research design. Data collection and processing from crunchbase corpus. Variables and measures. Predictive modeling techniques. Evaluation metrics. Results and analysis. Descriptive statistics and data visualizations: univariate analysis. Descriptive statistics and data visualizations: bivariate analysis. Correlation and regression analysis. Model comparison. Discussion.
References
Bibliografia: pp. 73-74.
| 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: | Data analysis for business |
| Thesis Supervisor: | Iafrate, Francesco |
| Academic Year: | 2022/2023 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 26 Sep 2023 16:11 |
| Last Modified: | 26 Sep 2023 16:11 |
| URI: | https://tesi.luiss.it/id/eprint/36539 |
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