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To offer the patients' medical care and successful outcomes, a successful hospital management is required. Traditional hospital management systems, which typically rely on manual procedures, are unable to handle the vast amount of data that is generated in the field of health in an effective manner. The statistical management method for healthcare systems that we discuss in this paper is based on machine learning. A significant amount of hospital-related data, including patient records, medical procedures, staffing levels, and resource distribution, are evaluated using the methodology, which makes use of automatic learning capabilities. The paper's objective is to create predictive models that, through patient demand projection, resource optimization, and resource improvement, assist hospital management in making better decisions. To correctly forecast future patient loads, bed occupancy rates, and staffing requirements, analyze previous data to identify patterns and trends. These predictions can assist hospital administrators in efficiently allocating resources, cutting down on patient wait times, and raising patient satisfaction. Additionally, our approach gives hospital administrators dashboards and visualizations that provide crucial performance indicators, merging data-driven decision-making, so they can monitor and assess the effectiveness of their management strategies. Utilizing automatic learning algorithms ensures the adaptability and scalability of the hospital management system. [ABSTRACT FROM AUTHOR]

In recent world data analytics is more popular term in the field of computer science. The paper focuses on the compressive survey of various data analysis tools to connect with the people by just reading their data. Some of the most popular data analysis tools have been chosen, and a comparison has been made based on some key parameters to determine the best tool in the data science industry. Scenes can uses a huge collection of data information to analyze, predict, and deliver information such as the whole determined by various countries, HEIs, government, universities, and schools. In this paper data analytics tools has taken in account with the consideration of different parameter as Cost, data handling capabilities, graphical capabilities, big data, and other considerations. Comparative study has been made using own experiences on various data analysis tools. [ABSTRACT FROM AUTHOR]

With the advent of large-scale datasets, visualization has become one of the most useful concepts of data mining, that assists researchers to identify data patterns without manually scanning each of the entry sets. To perform this task, researchers have proposed a wide variety of visualization techniques, that include, Online Analytical Processing (OLAP), different chart types, map types, different graph representation types, etc. But each of these methods vary in terms of their applicability, scalability, and data representation characteristics. Due to which it is difficult for researchers to identify optimum techniques for their application-specific use cases. Moreover, these models showcase variations in terms of their data handling & analysis capabilities, due to which researchers are needed to test & validate different visualization techniques for their deployments. These issues affect selection capabilities of these visualization models, which increases cost & time to market for the underlying product. To overcome these issues, a comparison of these models in terms of their context-specific nuances, application-specific advantages, deployment-specific limitations, and functional future scopes are discussed in this text. Upon referring this discussion, researchers will be able to identify optimum models for their application-specific use cases. This text also compares the models in terms of their performance metrics including delay, complexity, usability, and scalability values, which will further assist readers to identify performance-specific models for different deployments. Thus, this text can be used as a reference model for comparing different visualization tools & techniques for multiple application scenarios. [ABSTRACT FROM AUTHOR]