# Startup Sustainability Forecasting with Artificial Intelligence
## 3 Results
### Sustainability Forecasting for Greek Startups  
Through our collaboration with the aforementioned institutions, we collected an initial dataset of 44 startups. In the following paragraphs, we present several preliminary findings regarding the potential of machine learning methods applied to sustainability forecasting. The collected dataset contains features related to the progress of the 44 startups during an acceleration program. For each startup, the current income levels and whether they are currently profitable are recorded. The dataset also includes information on their technological readiness level (TRL), the current state of their MVP, and whether they exist in legal form. In addition, it records the number of founders and their participation in exhibitions, as well as evaluations of problem-founder fit and product-market fit. Finally, it details the quality of a comprehensive set of planning and presentation materials, including  
Figure 2: Architecture of the complete AI-driven startup sustainability forecasting system. The architecture is guided by findings regarding the available data sources and actual needs of professional investors. the market research report, business model canvas, value proposition canvas, business plan, financial plan, executive summary, website, social media, and pitch deck. The expert exectives of the acceleration program studied these startups in great detail and assigned a discrete sustainability score ranging from 1 to 3; scores of 1, 2, and 3 are interpreted as 'good,' 'average,' and 'bad,' respectively. The input features and the expert-assigned labels are considered in the upcoming supervised learning experiments. We also conducted experiments with seven ML algorithms for regression, using the RapidMiner (v2024.0.0) implementation. The methods are Linear Regression (LR), artificial neural network (ANN), decision tree (DT), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Gradient Boosting (GB), and Random Forest (RF) [20]. The models were configured with the recommended hyperparameters, and their performance was evaluated using leave-one-out cross-validation (LOOCV). During the validation process, the Root-Mean-Square Error (RMSE) and the standard deviation of the error (StdDev) were estimated. RMSE indicates the expected error of the model, whereas StdDev reflects the stability of the model. These metrics are provided in Equations (1)-(2), in which \(y_{i}\) and \(\hat{y}_{i}\) denote the actual and predicted scores for the \(i\)th startup, respectively. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{1}\]  
\[\text{StdDev}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}(y_{i}-\hat{y}_{i})^{2}- \text{RMSE}^{2}\bigr{)}^{2}} \tag{2}\]  
Table 3 presents the performance results for the aforementioned methods. Using leave-one-out cross-validation (LOOCV), we evaluate their ability to predict the sustainability scores assigned by experts. While every model displays an excellent ability to separate 'bad' from 'good' cases, which differ in score by 2, we observe several performance differences. The methods LR and ANN are less accurate than the other models, with an RMSE approximately equal to 0.665 and a StdDev of about 0.5. DT improves the RMSE, which reaches 0.578, but increases the variance to 0.518. The methods kNN and SVM outperform DT, achieving RMSEs of 0.534 and 0.505, respectively. They also significantly decrease the StdDev, to 0.392 for kNN and 0.378 for SVM. Finally, GB and RF are the top-performing methods, with very similar RMSE scores (0.489 vs. 0.487). However, GB has a significantly higher StdDev than RF (0.487 vs. 0.361). Therefore, these results indicate that RF is both the best-performing and most stable method for startup sustainability forecasting. To provide further insight regarding the workings of the model, a visualization of a good-performing decision tree is shown in Figure 3. By observing the rules, we infer that the tree first considers whether the startup is already profitable.