## 1 Introduction
It is evident that there is a large body of literature that has addressed this topic [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. However, few studies have analyzed the application of data mining approaches on a dataset that was obtained from enterprises in a transitional economy [28; 29; 30; 31]. There is a substantial and continuously expanding body of literature examining the use of advanced analytical techniques, such as data mining and machine learning, to enhance decision-making and strategic planning in a range of organizational contexts [32; 33]. These techniques have been applied across multiple sectors including finance, manufacturing, healthcare, retail, and telecommunications, and have often focused on enterprises operating in stable, well-established markets. Methodological progress in data mining including more sophisticated algorithms, scalable computing environments, and improved data preprocessing techniques has enabled researchers and practitioners to efficiently derive meaningful insights from large, complex, and multifaceted datasets [34]. Emerging research endeavors have begun to address this gap in the literature by systematically examining the application of data mining tools and techniques in transitional contexts [35; 36]. Recent studies have focused on identifying which methods best capture the nuances of organizational behavior and performance in these economies. They have also sought to determine how data mining can support enterprises grappling with uncertainty and constant change. The main goal of this current research was to analyze the application of multiple data mining approaches for predicting entrepreneurial intentions in enterprises. In the study, linear regression, logistic regression, QUEST decision tree, CHAID decision tree, support vector machine (SVM), and a feed-forward neural network were applied. Linear regression provides a means to understand how continuous predictors influence a continuous outcome variable. In this paper, it enabled the identification of linear relationships and the estimation of coefficients that highlighted the direction and magnitude of these effects. While it excels in clarity and interpretability, it may struggle when the data exhibit nonlinear patterns or complex interactions among variables [37]. Logistic regression is employed to examine binary outcomes, which allowed us to estimate the probability of events occurring. Its coefficients, expressed in log-odds, provide an interpretable lens through which managers can understand which predictors increase or decrease the likelihood of a particular outcome. However, this method assumes a linear relationship on the log-odds scale and may not fully capture nuanced, nonlinear patterns [38]. The QUEST decision tree analysis in this paper involved creating binary splits in the data to categorize observations and predict outcomes. It was designed to produce simpler, more statistically guided splits, making results more stable than some of the other tree methods. While it can expose meaningful data structures and interactions, the resulting tree may still require careful interpretation and validation to ensure its reliability [39]. Aside from the QUEST decision, a CHAID decision tree was applied. This employs chi-square tests to identify and form multi-way splits, creating distinct, data-driven categories. In the context of this paper, it helped uncover complex, categorical interactions, and groupings that might otherwise have remained hidden. Although CHAID can offer detailed segmentations, the resulting trees may become large and intricate, requiring careful scrutiny to avoid overfitting [40]. SVMs in this paper were applied to detect complex, often nonlinear patterns by using kernel functions to map data into higher-dimensional spaces. This flexibility can lead to strong predictive performance, especially when relationships are not easily described by simpler models [41].