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Leadership is essential for organizational success, driving innovation and growth. Yet, identifying leadership potential has often relied on subjective and biased methods. This paper introduces an objective solution using machine learning to revolutionize leadership detection. Leveraging the CRISP-DM methodology, the research progressed systematically from data analysis to model deployment. Random Forest, integrated with Principal Component Analysis (PCA) and balanced data, achieved a notable 90% accuracy in predicting leadership potential. This AI-driven approach overcomes human biases, uncovering hidden talents and providing a predictive, data-driven framework for leadership identification. This innovation reshapes traditional perceptions of leadership, fostering continuous growth and sustainable organizational success.

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Predicting Employees with Leadership Potential Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Fatima Zahra Abbour,
  • Soumaya Ounacer,
  • Soufiane Ardchir,
  • Mohamed Azzouazi,
  • Khadija Mahjoubi

摘要

Leadership is essential for organizational success, driving innovation and growth. Yet, identifying leadership potential has often relied on subjective and biased methods. This paper introduces an objective solution using machine learning to revolutionize leadership detection. Leveraging the CRISP-DM methodology, the research progressed systematically from data analysis to model deployment. Random Forest, integrated with Principal Component Analysis (PCA) and balanced data, achieved a notable 90% accuracy in predicting leadership potential. This AI-driven approach overcomes human biases, uncovering hidden talents and providing a predictive, data-driven framework for leadership identification. This innovation reshapes traditional perceptions of leadership, fostering continuous growth and sustainable organizational success.