In conclusion, international development has been one of the global community’s goals since the end of World War II, with developed countries searching for ways to alleviate the wealth disparity between the global north and the global south. Two primary methods of international development have been foreign direct investment (FDI) and foreign aid, with the former being a controversial tool and the latter more widely accepted. International development is not limited to financial aid donations. It also depends on human and social development, with programs including education, healthcare, infrastructure, social services, and economic aid. Although international development has brought significant benefits to many countries, it has faced criticism for promoting Westernization and cultural imperialism and for the effectiveness and accountability of development programs.

The future of international development requires innovative and collaborative approaches that address the root causes of poverty and inequality while respecting local cultures and values. Sustainable development practices need to be integrated into development programs to mitigate the impact of climate change on vulnerable communities. Collaboration between governments, non-governmental organizations (NGOs), and local communities is essential in creating long-term, sustainable development.

Machine learning implementation in international development can revolutionize development outcomes by improving the efficiency and effectiveness of aid programs. Through machine learning algorithms, aid organizations can analyze vast amounts of data to identify and address the most significant factors affecting poverty and inequality in the developing world. Moreover, machine learning can ease the monitoring and evaluation of aid programs, allowing organizations and watchdogs to assess program impact and make data-driven decisions about resource allocation.

This project utilized machine learning algorithms to discover the relationship between recipient nations’ socioeconomic and political characteristics and foreign aid allocation. The results showed that clustering algorithms could group countries into three clusters based on these characteristics. Other supervised methods, like SVM and neural networks, could predict levels of foreign aid received with relatively high accuracy. Algorithms like ARM, linear regression, decision trees, and naive Bayes uncovered the most significant factors in stimulating aid donations. Developing countries can use these results to target areas that promote aid allocation by wealthy nations.

In summary, the future of international development lies in innovative and collaborative approaches that focus on sustainable development practices while respecting local cultures and values. Machine learning can play a crucial role in improving the effectiveness and efficiency of aid programs. Additionally, algorithms like association rule mining, decision trees, naive Bayes, and linear regression can help target aid programs to countries based on their specific needs and characteristics. By working together, governments, NGOs, and local communities can create long-term, sustainable development that benefits all members of society.