The study aimed to build a forward-looking vision for educational leaders in building a humane society in the age of artificial intelligence and preparing and training a knowledge-based society. The future is considered in preparing that future and enabling scientific research skills and various scientific and wise issues. The problem is available and there are desired ones and can attend by investing in artificial intelligence applications and software to improve time, effort, cost, quality and effectiveness in achieving achievement and accuracy. Certainly, this is a smart tool that supports its strong efforts that only drain the time of the team for creativity and innovation, knowledge and coordination of work to invest the smart revolution in developing various skills, as the world has witnessed radical changes and complete progress in the field of artificial intelligence, which imposes new challenges and an opportunity not available in November, especially educational milk. In this context, building a balanced human society in the era of artificial intelligence requires a pioneering part of educational leadership, apart from the education path and developing future skills among students, by analyzing and reviewing the latter and previous studies during the current general educational literature (2023/2024) to ensure the emergence of a therapeutic vision that addresses a wide range of issues. The current study differs from previous studies in that it relies on the analytical considering the results of the following studies with clear modern features. A vision was built that is a basic supervisory vision to support students and build a knowledge society and enhance development. The vision was based on five pillars: a main leadership leader who builds skills. The educational leader as a guide activates community leadership. The value-based educational leader instills animal values. The community educational leader participates in community awareness and learning dialogues. A vision is managed to identify its degree of suitability from a specific point of view and expert presentation. The group of experts decides to anticipate the future, where partial educational leaderships occupy a pivotal role in building a human society that flourished in the era of artificial intelligence, a society characterized by coexistence between humans and humans, and relies on comprehensive cognitive animal values.

This study investigates the critical factors influencing investment in renewable energy projects and their subsequent impact on development across various countries. By examining government policies, financial incentives, technological advancements, and market dynamics, the research aims to provide valuable insights for policymakers, investors, and stakeholders seeking to accelerate the transition to a sustainable energy future. The analysis delves into case studies from Spain, Côte d'Ivoire, Canada, and China, highlighting the diverse factors that have contributed to their renewable energy success. In Spain, abundant solar and wind resources, coupled with supportive government policies, have driven significant growth in the sector. Côte d'Ivoire, facing electricity shortages, has recognized renewable energy as a crucial solution to meet increasing demand. Canada's reliance on hydropower and recent advancements in wind and solar energy have positioned it as a leader in clean energy. China, with its massive scale and ambitious targets, has demonstrated the potential for rapid renewable energy deployment. The findings from these case studies offer valuable lessons for countries seeking to replicate or surpass their achievements. By understanding the determinants of investment and their impact on renewable energy development, policymakers can design effective strategies to promote sustainable energy transitions and achieve economic, environmental, and social benefits.

This article provides a comprehensive guide to building robust regression models using Python's Scikit-learn library. It delves into the core concepts of regression, exploring various algorithms and their applications. By leveraging machine learning techniques, readers will gain insights into effective model selection, training, and evaluation. The article emphasizes practical implementation, providing code examples and real-world use cases. This resource empowers you to harness the power of regression modeling for accurate predictions and informed decision-making. In this study, the performance of various regression models was systematically assessed using key evaluation metrics, specifically Mean Squared Error (MSE) and R² score. The analysis identified the Decision Tree Regressor and Gradient Boosting Regressor as exhibiting superior predictive accuracy, characterized by low MSE and high R² values. These models demonstrated a robust fit to the dataset, positioning them as optimal choices for prediction tasks. Nonetheless, the selection of the final model should also consider factors such as interpretability, computational demands, and application-specific requirements. This thorough evaluation process aims to ensure the adoption of the most effective and suitable model, thereby improving the reliability and precision of predictions.