The article considers the application of complex marketing analysis to predict the sales volume of an organi-zation, in order to plan the optimal amount of costs and assess the risks of making marketing decisions. The given method is not the usual prediction of the indicator by indicator, and takes into account the interaction of the studied indicator with various market factors. An information technology corresponding to the method was developed using the Ms Excel package. Keywords: sales volume; factor of influence; trend; prediction; correlation; prediction error

This discourse explores the factors infl uencing the gradual adoption of IPv6, This discourse explores the factors infl uencing the gradual adoption of IPv6, the latest iteration of the Internet Protocol, despite its technological advantag-the latest iteration of the Internet Protocol, despite its technological advantages over IPv4. IPv6's expansive address space, accommodating the anticipated es over IPv4. IPv6's expansive address space, accommodating the anticipated surge in connected devices, makes it pivotal for sustained Internet growth. Not-surge in connected devices, makes it pivotal for sustained Internet growth. Notwithstanding, only 22% of websites have transitioned to IPv6 as of September. withstanding, only 22% of websites have transitioned to IPv6 as of September. The analysis delves into key impediments, including IPv4 resilience technologies, The analysis delves into key impediments, including IPv4 resilience technologies, compatibility challenges, costs, and ISP unreadiness. Additionally, potential solu-compatibility challenges, costs, and ISP unreadiness. Additionally, potential solutions, such as government intervention, stakeholder collaboration, and thorough tions, such as government intervention, stakeholder collaboration, and thorough testing, are proposed to expedite IPv6 adoption for a resilient and technological-testing, are proposed to expedite IPv6 adoption for a resilient and technologically advanced Internet infrastructure. ly advanced Internet infrastructure.

This article explores Python's prominence in Data Science, Data Analytics, and Machine Learning, attributing its widespread adoption to its user-friendly na-Machine Learning, attributing its widespread adoption to its user-friendly nature, robust online community, and powerful data-centric libraries such as Panture, robust online community, and powerful data-centric libraries such as Pandas, NumPy, and Matplotlib. It delves into the challenges of managing extensive das, NumPy, and Matplotlib. It delves into the challenges of managing extensive datasets and emphasizes the importance of memory utilization in navigating datasets and emphasizes the importance of memory utilization in navigating substantial data. The Pandas library's info() and memory_usage() methods are substantial data. The Pandas library's info() and memory_usage() methods are discussed as essential tools for assessing and optimizing dataframe memory con-discussed as essential tools for assessing and optimizing dataframe memory consumption. The article demonstrates how changing data types, particularly for sumption. The article demonstrates how changing data types, particularly for object columns, to the category datatype signifi cantly reduces memory usage object columns, to the category datatype signifi cantly reduces memory usage without altering the dataframe's appearance. The strategic adjustment of numer-without altering the dataframe's appearance. The strategic adjustment of numerical column data types based on value range, illustrated with the age column as ical column data types based on value range, illustrated with the age column as an example, is explored as a means of achieving precision and memory effi cien-an example, is explored as a means of achieving precision and memory effi ciency. The article highlights the considerable reduction in memory requirements cy. The article highlights the considerable reduction in memory requirements by transitioning from fl oat64 to fl oat16 for columns containing fl oating-point by transitioning from fl oat64 to fl oat16 for columns containing fl oating-point numbers. Overall, this comprehensive exploration provides valuable insights numbers. Overall, this comprehensive exploration provides valuable insights into effective strategies for memory optimization in Pandas dataframes, catering into effective strategies for memory optimization in Pandas dataframes, catering to both categorical and numerical data, contributing to enhanced computational to both categorical and numerical data, contributing to enhanced computational effi ciency and signifi cant memory savings.effi ciency and signifi cant memory savings.

Commercial websites are one of the most common types of Internet resources, tools and methods of design and analysis of which are actively developing. The analysis shows that this development takes place in two directions: 1) development of new and improvement of existing hardware and software of the commercial site; 2) development of new methods and models for the study of the functioning of the commercial site. Ensuring and maintaining high consumer traffic is the main objective of a com-mercial website. Despite the variety of software systems and platforms, their use requires significant resources of the site server and does not ensure its normal operation in conditions of high traffic. This fact makes it relevant to improve existing and develop new software systems. In this paper we developed a method of improving the software of a commercial site and the corresponding software product, which is based on the use of Python programming system. On the basis of web frameworks Django and Flask, designed hybrid CMS system for site content management, developed its file structure and user interface. A number of new and modified software modules have been built into the software to ensure its effective functioning. It is shown that the new software system provides work with the site server with different from the existing logic and allows more efficient use of its hardware resources. The developed software has been tested for the website of the online store, which is designed on the basis of the methodology proposed by the author. The results of testing show that even in the case of high user traffic significantly reduces the load on the hardware resources of the site server. The second major problem of electronic Commerce is the problem of ensuring, assessing and forecasting user traffic website and also, Issledovanie effective funct-ioning of the whole site. The analysis shows that this problem is solved in two main ways: 1) the use of web statistics tools located on the site, which make it possible to monitor the main performance indicators of the site during the operation; 2) the use of analytical methods and models that can be used to assess and predict the functioning of the site at the design stage. Website performance indicators obtained by web statistics tools are statistical estimates of random variables used by management to optimize the structure and process of site navigation. These data characterize certain aspects of the process of visiting the site by the user, but do not take into account the random nature and dynamics of the process and do not give a complete picture of the traffic and functioning of the commercial site. Studies have shown that it is equally important to evaluate user traffic and the functioning of a commercial site at the stage of its design. This will give the opportunity to choose the right hardware and software site, make a forecast for the future, to assess the feasibility of investments and investment volumes. The solution to this problem are analytical and statistical models, which are based on the application of the theory of Queuing and Markov processes and investigate the behavior of a discrete system such as “userSait”. These models, although taking into account the random nature of traffic and the functioning of the site, but require reliable estimates of a large number of probabilistic indicators and significant computational procedures, which complicates their practical application. The paper proposes two statistical models to evaluate the user traffic of a commercial site. The models allow quantitative description of traffic dynamics, are free from the shortcomings of existing methods and are convenient for practical use. Approbation of models is carried out for statistical data of the online store designed on the basis of the software developed in the dissertation. Methods of regression, statistical and variance analysis evaluated the parameters of the models, the adequacy and statistical reliability of the models. Practical application of models makes it possible to make a forecast for the future, to assess the feasibility of investing in a commercial site. Models, with a small modification, can also be used to analyzis user traffic of non-commercial saits. The paper also developed a methodology and appropriate information technology for the study of probabilistic and statistical characteristics and traffic structure of the online store in the established mode of operation. It is shown, that in this mode, user traffic is a time series of stationary random process, with normal data distribution law, which contains only a random component and its elements can be considered as random oscillations around a certain average value. The obtained results can be used in further studies to build autoregressive models for predicting the user traffic time series.