The article discusses the need to develop a marketing strategy for the automobile industry of the Republic of Uzbekistan, proposes a model of marketing strategy, as well as a standard model for determining the regression weight coefficient. Key words: strategy, marketing strategy, marketing strategy development models, marketing strategy structure, regression weight coefficients.

This study explores the use of machine learning (ML) and deep learning (DL) models for predicting stock price movements through sentiment analysis of financial news articles. Four models were evaluated: Random Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results showed that deep learning models, particularly BERT, outperformed traditional ML models, achieving higher accuracy, precision, recall, and F1 scores. BERT’s ability to capture contextual relationships in text proved superior in handling the complexities of financial news. This research highlights the effectiveness of sentiment analysis in stock market prediction and suggests that advanced ML and DL techniques can enhance forecasting accuracy. Future work could focus on refining these models by integrating more data sources and exploring hybrid approaches.

This article discusses the problem of the role of the cluster in the process of forming a sustainable business and maintaining the innovative development of the national economy, including its individual industries in modern conditions. In the course of writing the article, various analytical methods were used: comparative and retrospective analysis, the method of data compilation, induction and deduction, as well as modeling. As a result of a generalized analysis of the activities of clusters and, in general, the activities of groups of companies, it was concluded that the importance of cluster associations is significant in the overall structure of mechanisms to support the development of innovations. The author analyzed various approaches to the formation of cluster policy by determining the effect of the resulting synergetic features of the cluster form of doing business, from the point of view of the formation of business sustainability and strengthening the position of the company's brand.

In this study, we conducted a comparative analysis of traditional machine learning models and advanced deep learning models for sentiment analysis of consumer feedback, aiming to assess their impact on business strategies. We evaluated the performance of Random Forest, Support Vector Machines (SVM), Naive Bayes, BERT, and GPT models using a comprehensive dataset derived from e-commerce platforms, social media comments, customer surveys, and online forums. Our results demonstrated that while traditional models like Random Forest and SVM achieved decent accuracy, they were outperformed by the large language models, BERT and GPT. BERT achieved the highest accuracy (92.7%), precision (91.3%), recall (94.2%), and F1-score (92.7%), showcasing its exceptional ability to capture contextual relationships in text. GPT also demonstrated strong performance with an accuracy of 91.5%, although slightly lower than BERT. The findings suggest that transformer-based models, particularly BERT, offer significant advantages in processing consumer feedback, enabling businesses to extract more accurate insights for decision-making, customer satisfaction improvement, and marketing optimization. This study emphasizes the importance of adopting deep learning models for sentiment analysis in business contexts while acknowledging the potential limitations related to computational resources. Ultimately, our research highlights the value of sentiment analysis in informing business strategies and enhancing customer engagement.

The post-Brexit era has introduced significant economic risk and uncertainty for the United Kingdom, influencing various sectors from trade and investment to labor markets and financial services. The withdrawal from the European Union (EU) has disrupted established trade agreements, created new regulatory frameworks, and heightened concerns about economic stability. This paper examines the key challenges facing the UK economy in the post-Brexit landscape, focusing on the areas of trade, foreign direct investment, labor mobility, and economic growth. Through a comprehensive analysis of economic data, policy changes, and industry reports, this study explores the primary sources of uncertainty, including shifts in global trade dynamics, changes to supply chain logistics, and the impact of fluctuating exchange rates. The paper also discusses potential pathways forward for the UK, offering recommendations for mitigating risks, fostering economic resilience, and navigating future challenges. It emphasizes the importance of policy reforms, strategic partnerships, and innovation to safeguard the long-term stability of the UK economy in a post-Brexit world.