Название: Data Analytics and Machine Learning: Navigating the Big Data Landscape
Автор: Pushpa Singh, Asha Rani Mishra, Payal Garg
Издательство: Springer
Серия: Studies in Big Data
Год: 2024
Страниц: 357
Язык: английский
Формат: pdf (true)
Размер: 10.7 MB

This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, Big Data, and Machine Learning solutions in their own organizations. The book discusses the transformative power of data analytics and Big Data in various industries and sectors and how Machine Learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how Big Data explosion, the power of analytics and Machine Learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, Big Data, and Machine Learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.

Data has become the main driver behind innovation, decision-making, and the change of many sectors and civilisations in the modern period. The dynamic trinity of Data Analytics, Big Data, and Machine Learning is thoroughly introduced in this chapter, which also reveals their profound significance, intricate relationships, and transformational abilities. The fundamental layer of data processing is data analytics. Data must be carefully examined, cleaned, transformed, and modelled in order to reveal patterns, trends, and insightful information. A data-driven revolution is sparked by big data. In our highly linked world, data is produced in enormous numbers, diversity, velocity, and authenticity. The third pillar, machine learning, uses data-driven algorithms to enable automated prediction and decision-making. This chapter explores the key methods and equipment needed to fully utilise the power of data analytics and also discusses how technologies used in Big Data management, processing, and insight extraction. A foundation is set for a thorough investigation of these interconnected realms when we begin the chapters that follow. Data analytics, Big Data, and Machine Learning are not distinct ideas; rather, they are woven into the fabric of modern innovation and technology. This chapter serves as the beginning of this captivating journey, providing a solid understanding of and insight into the enormous possibilities of data-driven insights and wise decision-making.

Hence in this paper a predictive model using machine learning-based linear regression algorithm is used. The study is done using a popular statistical tool named R programming. In this study the R tool is explored and represented with utility for recommendation system designing and finding insights from data by showing various plots. The results are formulated and presented in a formal and structured way using the R tool. During this study it has been observed that the R tool has potential to be one of the leading tools for research and business analytics.

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