The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

Infrastructure development policies have been criticised for lacking a deliberate pro-gender and pro-informal sector orientation. Since African economies are dual enclaves, with the traditional and informal sectors female-dominated, failure to have gendered infrastructure development planning and investment exacerbates gender inequality. The paper examines the effect of the infrastructure development index, the size of the informal economy, and the level of economic development on gender inequality. The paper applies the panel autoregressive distributed lag method to data on the gender inequality index, infrastructure development index, GDP per capita, and size of the informal sector for the period 2005–2018. The sample consists of 44 African countries. The research established that the infrastructure development index, its sub-indices, GDP per capita, and the size of the informal sector are crucial dynamics that governments need to consider carefully when formulating development policies to reduce gender inequality. The research found that investment in infrastructure in general, transport infrastructure, and energy infrastructure reduces gender inequality. infrastructure development has gender inequality increasing effects in some countries and gender inequality reducing effects in others. The pattern suggests that at the continental level a Kuznets-type patten in the relationship between gender inequality and infrastructure development, gender inequality and size of informal sector, and gender inequality and GDP per capita exists. Some countries are in the region where changes in these covariates positively correlate with gender inequality, while others are in the region where further increases in the covariates reduce gender inequality.

With the characteristics of resisting business cycle, mitigating cash flow, and improving portfolio resilience, special assets usually enter a highly active period in the economic downturn cycle, and gradually become an effective asset allocation means in the transition phase of the business cycle. This article aims to analyze the importance of the development of China's special asset investment industry in the context of high-quality economic development, and explore how to introduce market-oriented mechanisms to build primary and secondary markets for special assets, in order to improve the effective allocation of market resources and maximize returns.

The purpose of the study is to create proposals and recommendations to improve the system evaluating the quality of governance and efficient use of budget funds in order to improve public welfare and sustainable development. The research methodology included application of statistical methods to review scientific articles, legislative acts and other documents, study models for evaluating the quality of governance and efficient use of budget funds. Mathematical modeling and forecasting methods were also used to assess aspects of governance and predict the results when changes are made, including building a trend model and determining the forecast values of accrued taxes and mandatory payments for 2024–2026. The conclusions highlight there is a positive correlation between the accrued taxes and mandatory payments to the budget of the Republic of Kazakhstan, and an economic growth and changes in tax legislation. The key factors influencing the quality of governance and efficient use of budget funds were identified. Recommendations were developed to improve the quality assessment system and governance of budget funds in order to increase efficiency and responsibility in financial management. The results of the study can be used by public administration bodies and financial institutions to optimize the governance of budget funds.