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.

Based on the research on 31 provincial-level administrative regions at the end of 2022, we used the geographic concentration index, geographic imbalance index, SPSS and ARCGIS spatial analysis techniques to study the spatial distribution, distribution factor correlation, and accessibility of national 5A-level scenic spots. The research results show that the overall distribution of my country's 5A-level scenic spots is unbalanced, with a low degree of concentration, showing a pattern of denseness in the east and sparseness in the west, with large inter-provincial differences. The density of traffic highways is positively correlated with the distribution density of 5A-level scenic spots. The traffic lines in the central and eastern regions are dense, and there are a large number of 5A-level scenic spots, especially the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and the middle and lower reaches of the Yangtze River and Yellow River. Therefore, the spatial distribution of China's 5A-level tourist attractions is mainly affected by the interaction of economic, transportation and social factors, among which GDP, transportation network and attraction of scenic spots are the most critical factors. These research results can provide a reference for optimizing the spatial layout of China's scenic resources and promoting regional socio-economic development.

Carbonated soft drinks (CSDs) have long been a mainstay of the beverage business but changing consumer tastes and rising health awareness have necessitated a thorough study of the variables impacting consumer choices. This study intends to explore the complex web of customer preferences, purchasing behaviour, and perceptions related to carbonated soft drinks. This research analyses how numerous variables, including gender, affect these preferences and choices via careful examination. The purpose of thepresent research is to determine the perception of consumer influencing customer choice preferences for the consumption of carbonated soft drinks, influence of gender and the role of advertisement in finalizing the choice. It would be helpful to do further research to better understand how these highlighted variables affect purchasing choices, especially gender-based variances. The important influence of gender on consumer behaviour has been acknowledged. For this study, a structured questionnaire was distributed through online social media to individuals of 12–45 years of age from the period of April–May 2023. For analysis of the data collected, SPSS 22.0 was used. The study has confirmed that consumption of Coca-Cola is higher than any other soft drink in almost the entire country. The factors like youthfulness, tradition, status symbol and level of carbonation have different influences on the buying behavior of male and female consumers.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

This research presents a bibliometric review of scientific production on the social and economic factors that influence mortality from tuberculosis between the years 2000 and 2024. The analysis covered 1742 documents from 848 sources, revealing an annual growth of 6% in scientific production with a notable increase starting in 2010, reaching a peak in 2021. This increase reflects growing concern about socioeconomic inequalities affecting tuberculosis mortality, exacerbated in part by the COVID-19 pandemic. The main authors identified in the study include Naghavi, Basu and Hay, whose works have had a significant impact on the field. The most prominent journals in the dissemination of this research are Plos One, International Journal of Tuberculosis and Lung Disease and The Lancet. The countries with the greatest scientific production include the United States, the United Kingdom, India and South Africa, highlighting a strong international contribution and a global approach to the problem. The semantic development of the research shows a concentration on terms such as “mortality rate”, “risk factors” and “public health”, with a thematic map highlighting driving themes such as “socioeconomic factors” and “developing countries”. The theoretical evolution reflects a growing interest in economic and social aspects to gender contexts and associated diseases. This study provides a comprehensive view of current scientific knowledge, identifying key trends and emerging areas for future research.