## 3 Results
### Result Validation  
To validate the results obtained from the Social Network Analysis (SNA), an additional gradient boosting regression analysis was conducted. Gradient Boosting Regression (GBR) is a robust machine learning method designed mainly for regression and classification tasks. It constructs an ensemble of weak learners, usually decision trees, and integrates them to form a strong predictive model. The core idea of gradient boosting lies in iteratively addressing the errors made by earlier models in the ensemble, progressively enhancing the model's accuracy with each step [58; 59; 60]. The purpose of this validation was to verify whether the SNA results regarding key vehicle attributes influencing decisions in a peer-to-peer car-sharing platform aligned with findings from a machine learning approach. GBR was selected for its ability to model complex, non-linear relationships, making it an ideal tool to test the robustness of the SNA-derived insights. The dataset used for the gradient boosting validation contained the same 1451 responses that were utilized in the SNA. The features from SNA, including degree centrality, closeness centrality, betweenness centrality, density, and average path length, were employed as input variables. These features reflect critical technical vehicle factors like fuel consumption, safety, and technological advancement. The target variable was the decision to rent a vehicle, coded as a binary outcome (1 for renting, 0 for not renting). Then, to validate the SNA results, a GBR classifier was implemented using Python's XGBoost library. The GBR model iteratively builds decision trees to minimize errors and improve prediction accuracy with each iteration [61]. The key steps for this analysis are detailed below:  
1. Data Splitting--The dataset was divided into training (80%) and test sets (20%) to evaluate model performance. 2. Model Parameters--The gradient boosting model was initialized with a learning rate of 0.1, 100 estimators, and a maximum depth of 5 to ensure that the model balanced performance and overfitting risk. 3. Model Training and Testing--The model was trained on the training dataset, optimizing for accuracy in predicting the decision to rent a vehicle based on the technical factors. The accuracy of the model on the test set was calculated to evaluate its effectiveness in capturing the relationship between technical vehicle attributes and rental decisions. The model achieved an accuracy of 86%, indicating that it effectively modeled the decision-making process based on the technical factors. One of the key outcomes of gradient boosting analysis is the ability to extract feature importance, which allows for a comparison with the SNA results. Below are the feature importance values derived from the gradient boosting model as presented in Table 5.  
The results from the gradient boosting model strongly align with the findings from SNA. Both methods identified betweenness centrality as the most important factor influencing vehicle selection, further emphasizing the aspects of vehicles that serve as intermediaries connecting various crucial attributes (such as fuel efficiency and safety features). \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Importance [\%]** \\ \hline Betweenness centrality & 38.12 \\ Closeness centrality & 21.45 \\ Degree centrality & 15.37 \\ Density & 12.91 \\ Average path length & 12.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A gradient boosting model. Additionally, closeness centrality and degree centrality emerged as the next most important features in both SNA and gradient boosting. This alignment validates the use of SNA in identifying the most relevant technical vehicle features for a peer-to-peer car-sharing platform. The gradient boosting analysis confirms that focusing on these central vehicle attributes, such as safety and efficiency, is essential for improving user decision-making and optimizing platform offerings.