## 4 Results and Evaluations  
The use of performance data in the public sector can be enhanced by decentralized structures, promoting a culture of training and development through networks and collaborative efforts. Emerging technologies like ML and AI are adept at processing extensive and diverse datasets in leadership research. However, understanding the relationship between leadership effectiveness and traits through these technologies is less straightforward compared to traditional methods. Therefore, it is crucial to consider methodological approaches and novel methods in leadership research.  
Machine learning demonstrates superior performance with large and diverse datasets. Human resource data encompass a wide variety and diverse types of data sources related to candidates. Machine learning algorithms enhance learning and generalization capabilities with a broad spectrum of data sources. Machine learning can predict future events based on past data, providing a significant advantage in making strategic decisions in human resource management.  
This study was conducted to identify potential leadership candidates using data-driven systems in HR. Identifying potential leadership candidates is important in terms of addressing the career development process with a transformational leadership approach.  
In this study, we investigated the importance of transformational leadership in HR management and the use of ML and AI techniques to identify potential leadership candidates in large-scale organizations. The forecast model developed by using a dataset of 5000 employees in the PTT achieved a 96% success rate with the kNN algorithm, demonstrating that big data and ML algorithms can be an effective tool for identifying leadership candidates.  
Predicting potential leadership candidates is an important step in HR management and leadership development practices. In the public sector and other large-scale businesses, it will be possible to create more objective and data-driven leadership candidate selection and development programs using big data and ML algorithms, taking into account the corporate culture, and thus, it will be possible to train visionary and inspiring leaders with transformational leadership skills in a sustainable way, ensuring competitive business management. Therefore, this study will make significant contributions to the literature in terms of using artificial intelligence and machine learning algorithms in leadership research on public institution data and making forecasts that will benefit the public sector. If potential leadership candidates can be identified with the help of a decision support model, the competencies that the candidates will need can be planned for and gained during in-service training and experience during their careers.  
It is estimated that data such as salary information that could not be provided in the study and the ratio of salary differences between titles can also be effective in the analysis. This study is based on the limited human resource dataset of the PTT, which excludes personal data, and the results and evaluations are limited to this scope. Moreover, future studies can explore how machine learning can be applied to areas like leadership training, leadership behavior, and the leadership process.