## 6 Conclusions
### Limitations  
It is important to mention that even though the dataset provided as a part of this paper can be considered as being relatively wide feature-wise, it is still far from complete. A large amount of data that could significantly influence the prediction models is not publicly available. For starters, we lack the data from the contestants' application forms, in which the contestants explicitly provide information on their personal traits. Additionally, we do not have any information describing a contestant's quizzing skill level, which could be a key factor for accurate predictions. To compensate for this, we used data on the cash builder result and included whether the chasers recognized the contestant, assuming they were more familiar with skilled individuals. Furthermore, for both the selected offer prediction and the outcome prediction, deriving simple rules using tree-based methods was not feasible. This was primarily because of the complexity of the generated trees and, more importantly, the relatively small size of our dataset, which limited the rule extraction. Although the total number of samples in the dataset is relatively low for machine-learning tasks, the dataset provided sufficient information for the models to perform well. To prevent overfitting during the model training, we used cross-validation for the neural networks and pruning of the decision trees. The built-in bootstrapping mechanism helped to reduce overfitting in random forests. Although some of the limitations presented in this subsection could conceivably be mitigated by increasing the dataset size, we are currently unable to do so. For the Croatian version of the show, all the available episodes that are a part of complete seasons were included in the analysis at the time of the publication of this paper. In contrast, international versions of the show are not directly applicable to this study for several reasons. Primarily, we are not able to make categorical claims about the influences of cultural differences on the findings of this study, as such differences may, indeed, play different roles. In addition, other versions of the show can differ significantly in terms of the rules and propositions of the game. For example, the British version features different chasers across seasons and even varies in the number of chasers (some seasons include six). The German version features seven chasers; the Australian version, four chasers; and the Finnish version, only three chasers. International versions with five chasers might be applicable to our research (e.g., the Czech or Dutch versions), but it still remains unclear whether the offers in various versions of the show are commensurate with those in the Croatian version in terms of the prize money and chasers' time, and whether contestants' strategies or preferences are influenced by cultural factors. Many contestants also mentioned the amount of money they would be satisfied with. This information could play a significant role in the offer-tailoring process but was excluded because of a high number of missing values. Furthermore, some of the age groups, educational levels, or fields of work lack higher representability. Although we were able to draw valid conclusions from the dataset used in this research, one could easily claim that the set itself contains a relatively low number of samples (171) for further and more advanced analyses. Despite these limitations, the dataset serves as a strong starting point for further research, providing valuable insights and establishing a foundation for future studies with more comprehensive data. Another factor that could play a key role in the further improvement of the models' performances is the psychological state of the contestants. People react to stress differently. Potential anxiety, stage fright, or an adrenaline rush could lead to cognitive blocks or impulsive reactions that could result in poor performance. Unfortunately, this information is impossible to obtain accurately by observing the contestants.