Data Analytics and Machine Learning

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.

This chapter gives a brief overview of the fundamentals and lifecycle of data analytics. The foundation for the present stage of technology, data analytics systems is ranged over in this chapter. The chapter also delves into detailing open-source tools such as Power BI and Tableau used in developing data analytics systems. Traditional analysis is different from big data analysis in terms of volume and data processed varieties. To meet the requirements, various stages are required to put in order the activities involved in the processing, acquisition, reuse, and analysis of the given data. The lifecycle for data analysis will help to manage and organize the tasks connected to big data research and analysis. Data Analytics evolution with big data analytics, SQL analytics, and business analytics is explained. Furthermore, the chapter outlines the future of data analytics by leveraging its fundamental lifecycle and elucidates various data analytics tools.

This chapter functions as a practical guide for constructing predictive models using machine learning, focusing on the nuanced process of translating data into actionable insights. Key themes include the selection of an appropriate machine learning model tailored to specific problems, mastering the art of feature engineering to refine raw data into informative features aligned with chosen algorithms, and the iterative process of model training and hyperparameter fine-tuning for optimal predictive accuracy. The chapter aims to empower data scientists, analysts, and decision-makers by providing essential tools for constructing predictive models driven by machine learning. It emphasizes the uncovering of hidden patterns and the facilitation of better-informed decisions. By laying the groundwork for a transformative journey from raw data to insights, the chapter enables readers to harness the full potential of predictive modeling within the dynamic landscape of machine learning. Overall, it serves as a comprehensive resource for navigating the complexities of model construction, offering practical insights and strategies for success in predictive modeling endeavors.

Recent innovations in agriculture have made it smarter, more intelligent, and précised. Due to the technological advancement paradigm shift of agriculture practices from traditional to wireless digital incorporation of IoT, AI/ML, and Sensor technologies. Machine learning is a critical technique in agriculture for ensuring food assurance and sustainability. The machine learning algorithm starts from scratch to the final step—The selection of Crop, Soil Preparation, Seed Selection, Seed sowing, Irrigation, Fertilizer/Manure Selection, Control of Pests/weeds/diseases, Crop Harvesting, and Crop distribution for sales. ML algorithm suggests the right step for high-yield crops and precision farming. This article discusses how predictive ML supervised classification algorithms—especially K-Nearest Neighbor (KNN) can be helpful in the selection of crops, fertilizer to be used, corrective measures for the precision yield, and irrigation needs by looking at different parameters like climatic conditions, soil type, and previous crops grown in the field. The accuracy of algorithms comes out to be more than 90% depending on some uncertainties in the collection of data from different sensors. This results in well-designed irrigation plans based on the specific field conditions and crop needs.

In recent era, Big Data Streams have significant impact owing the reality that there are many applications from where a big amount of data is continuously generated at a bang-up velocity. Because of integral dynamical features of big data, it is hard to apply existing working models directly on big data streams. The solution of this limitation is data streaming. A modern-day data streaming architecture allows taking up, operating and analyzing high mass of high-speed data from a collection of sources in real time to build more reactive and intelligent customer experiences. It can be designed as a batch of five logical layers; Source, Stream Storage, Stream Ingestion, Stream Processing and Destination. This chapter comprises of a brief assessment on the stream analysis of big data which engaged a thorough and organized way to looking at the inclination of technologies and tools used in the field of big data streaming along with their comparisons.

We will provide study to cover issues like scalability, privacy and load balancing and their existing solutions. DGIM Algorithm which is used to count the number of ones in a window and FCM Clustering Algorithm and others are also in consideration to review in this chapter.

Image SR is a critical task in the field of computer vision, aiming to enhance the resolution and quality of low-resolution images. This chapter explores the remarkable achievements in image super-resolution techniques, spanning from traditional interpolation methods to state-of-the-art deep learning approaches. The chapter begins by providing an overview of the importance and applications of image super-resolution in various domains, including medical imaging, surveillance, and remote sensing. The chapter delves into the foundational concepts of classical interpolation techniques such as bicubic and bilinear interpolation, discussing their limitations and artifacts. It then progresses to explore more sophisticated interpolation methods, including Lanczos and spline-based approaches, which strive to achieve better results but still encounter challenges when upscaling images significantly. The focal point of this chapter revolves around deep learning-based methods for image SR. Convolutional Neural Networks (CNNs) have revolutionized the field, presenting unprecedented capabilities in producing high-quality super-resolved images. The chapter elaborates on popular CNN architectures for image super-resolution, including SRCNN, VDSR, and EDSR, highlighting their strengths and drawbacks. Additionally, the utilization of Generative Adversarial Networks (GANs) for super-resolution tasks is discussed, as GANs have shown remarkable potential in generating realistic high-resolution images. Moreover, the chapter addresses various challenges in image super-resolution, such as managing artifacts, improving perceptual quality, and dealing with limited training data. Techniques to mitigate these challenges, such as residual learning, perceptual loss functions, and data augmentation, are analyzed. Overall, this chapter offers a comprehensive survey of the advancements in image SR, serving as a valuable resource for researchers, engineers, and practitioners in the fields of computer vision, image processing, and machine learning. It highlights the continuous evolution of image SR techniques and their potential to reshape the future of high-resolution imaging in diverse domains.

Finance has been combined with technology to introduce newer advances and facilities in the domain. One such technological advance is cryptocurrency which works on the Blockchain technology. This has proved to be a new topic of research for computer science. However, these currencies are volatile in nature and their forecasting can be really challenging as there are dozens of cryptocurrencies in use all around the world. This chapter uses the time series-based forecasting model for the prediction of the future price of Ethereum since it handles both logistic growth and piece-wise linearity of data. This model is independent as it does not depend on past or historical data which contain seasonality. This model is suitable for real use cases after seasonal fitting using Naïve model, time series analysis, and Facebook Prophet Module (FBProphet). FBProphet Model achieves better accuracy as compared to other models. This chapter aims at drawing a better statistical model with Exploratory Data Analysis (EDA) on the basis of several trends from year 2016 to 2020. Analysis carried out in the chapter can help in understanding various trends related to Ethereum price prediction.

In this digital era all E-commerce activities are based on the modern recommendation systems where a company wants to analyse the buying pattern of its customers to optimize their sales strategies which mainly includes focusing more on valuable customers which is based on the amount of purchase made by customer rather than the traditional way of recommending a product. In the modern recommendation systems different parameters are synthesized for designing efficient recommendation systems. In this paper the data of 325 customers who have made certain purchases from a website having naive parameters like age, job type, education, metro city, signed in with company since and purchase history are considered. The E-commerce business model’s profit making is primarily dependent on choice-based recommendation systems. 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.

The emergence of the digital age has ushered in an unprecedented era of data production and collection, creating big data models. In this context, a valuable technique to address complex issues originating from big data analytics is deep learning, which is a subgroup of machine learning. The aim of this chapter is to give a thorough assessment of deep learning methods and how they are implemented in big data analytics. Beginning with an introduction to the fundamental tents of deep learning, including neural networks and deep neural architectures, the mechanisms by which deep models can automatically learn and represent complex patterns from raw data are explored. It examines various aspects of deep learning applications of big data analysis. It shows how deep learning models excel in feature learning, enabling the automatic extraction of valuable information from huge data sets. Finally, the chapter describes emerging trends in deep learning and big data analysis, providing a glimpse into the future of this dynamic field. It draws attention to the pivotal role that deep learning techniques have played in transforming the big data analytics environment and emphasizes the ongoing significance of research and innovation in this quickly developing discipline.

Recent innovations performed on data analytics technologies within the last two decades have steered towards a new level of data-driven decision-making in different industries. This chapter elucidates the significant aspects of data privacy with ethics under the dominion of data analytics. Firstly, the chapter details how imperative is protecting an individual's personal information. Secondly, discusses the legal frameworks, namely, GDPR (General Data Protection Regulation) and different data protection laws around the world, which have greatly influenced for bringing awareness on data privacy. Thirdly, how ethical considerations do compliment the outcome when these regulations are complied with. Finally, this chapter also offers information on how the organizations and its professionals must meticulously put efforts towards building a world on how to handle data ethically. In this regard, the chapter provides various instances from projects, case studies and real-world scenarios to support and discuss how data analytics do create positive and negative impacts amongst an individual and the society. To conclude, this chapter focuses on the vital aspects of mixing data privacy and ethics when working with data analytics. Furthermore, how organizations can follow holistic approaches wherein a blend of technology safety, legal frameworks and ethical awareness can be infused into their work culture when their employees are dealing with data in various projects in the future.

Modern technology has given rise to strong technologies like machine learning, big data, and data analytics that are revolutionising how businesses function and make choices. Business and marketing, healthcare, finance, manufacturing and supply chains, transportation and logistics, energy utilisation, are only a few of the disciplines where their practical applications are summarised in this chapter. Precision medicine has evolved greatly via genetic data analysis, and big data analysis of electronic health records (EHRs) allows for better patient treatment. AI and data analytics have a significant impact on risk assessment and fraud detection in the financial sector. Analytical methods in manufacturing and supply chain optimisation are highlighted in research articles. Significant progress has been made in lowering operating expenses and equipment downtime thanks to machine learning-driven predictive maintenance. Real-time monitoring and route optimisation in transport: the importance of data analytics. The advancements in safety and dependability include machine learning-powered autonomous cars and predictive maintenance strategies. Grid management and energy usage have been optimised by the energy industry via the use of big data and data analytics. Equipment breakdowns may be predicted and energy production efficiency increased using machine learning. Customised learning and evaluation via the use of data analytics. Content distribution and student engagement are aided by machine learning algorithms, such recommendation systems. To summarise, the fields of data analytics, big data, and machine learning have broad and extensive uses in a variety of fields. These applications have revolutionised decision-making processes, increased productivity, and stimulated creativity in the contemporary world. These innovations are very important in determining how different fields will develop in the future.

In the era of digitalization, we stand on the cusp of a data revolution. A staggering volume of data, sourced from manufacturing, banking, social media, e-commerce, healthcare records, and more, collectively known as Big Data, has inundated our world. Concerning the intelligent analysis of extensive datasets and the development of advanced applications for diverse domains, the crucial foundation lies in artificial intelligence (AI), placing specific emphasis on machine learning (ML) and deep learning (DL). The scale of data generation today is staggering, and the capabilities of these technologies are equally remarkable. In the realm of healthcare, they facilitate early disease detectionn customized treatments for patients, fundamentally transforming healthcare delivery. In the financial sector, analytics are shaping investment strategies, while in agriculture, they optimize resource allocation and crop yields. With data-driven insights enhancing transportation, energy management, and infrastructure systems in urban planning. These cutting-edge technologies collectively empower us to unlock valuable insights, reduce costs, streamline operations, and make data-driven decisions, across technical applications. This chapter conducts a comprehensive exploration of the profound impact of Data Analytics, Big Data, and AI in harnessing this data wealth across real-world applications and delves into various algorithms and techniques employed in ML, DL, and analytics.

AI Tool, a powerful large language model (LLM), is designed to create human-like responses in natural language conversations, leveraging extensive training on internet data for information, engagement, task assistance, and creative insights.AI Tool’s core is a transformer neural network, renowned for capturing text’s long-range dependencies. With 175 billion parameters, it’s among the most extensive LLMs to date. This research endeavors to present a holistic perspective on the responses generated by AI Tool in a variety of industrial sectors, all while aligning with data analytic principles. To ensure the reliability of its responses, the study engaged human experts in respective fields to cross-verify the outcomes. Furthermore, to gauge the performance of AI Tool, the study meticulously considered specific parameters and conducted a thorough evaluation. The findings of this research serve the research community and other users, by offering insights into the applications and interaction patterns of AI Tool within the context of data analytics. The results affirm that AI Tool is capable of producing human-like responses that are both informative and engaging, all within the framework of data analytics. However, it’s crucial to acknowledge that AI Tool may occasionally produce inaccurate or nonsensical answers. Consequently, a critical evaluation of AI Tool’s information, coupled with verification from reliable sources, when necessary, is imperative. Despite these considerations, this study underscores AI Tool’s potential as a promising tool for natural language processing, with applications spanning a wide array of fields, particularly when integrated with data analytic concepts.

Global lung cancer mortality is growing. This supports early cancer screenings. CT lung nodule segmentation is complicated and affects medical research, surgical planning, and diagnostic decision support. All are complex issues with important applications. Machines and humans struggle to split non solitary nodules with uncertain boundaries. Since segmentation has distinct limits, single nodules are easier to divide. Several researchers have proposed CT-based lung evaluation algorithms. Growing imaging datasets and the need to swiftly and precisely define normal and diseased lung lobes are the reasons. Multi-process lung segmentation methods with manual empirical parameter modifications are common. First lung slice and nodule segmentation using ML and DL is essential for cancer detection. This detects cancer at various stages. Deep learning techniques have improved healthcare image analysis. There are few deep learning approaches like ResNet 50,101, VGG16, Autoencoders, U-Net with modifications, and graph convolutional networks to classify lung nodules, COVID-19, and pneumonia. This chapter includes a summary of datasets that are open to the public and are the primary resources utilized by scholars working in this area. A direct look into the field of diagnosing lung disorders is what we hope to achieve with the information provided in this chapter.

The fusion of Data Analytics, Big Data, and Machine Learning has become a powerful force in the always-changing world of data-driven decision-making. This chapter offers a brief overview of their practical uses, illuminating how these technologies are reshaping markets and driving creativity. The cornerstone, data analytics, is studied first, emphasizing its capacity to extract useful insights from a variety of sources. To demonstrate how Data Analytics enables organizations to optimize processes, improve consumer experiences, and manage risks through data-driven decision-making, real-world examples from industries including e-commerce, finance, and healthcare are shown. Next, Big Data takes center stage to demonstrate its ability to handle enormous amounts of data. We examine its uses in industries ranging from urban planning to agriculture, showing how it facilitates better decision-making through data-driven insights. The third element of the equation, machine learning, emerges as a crucial enabler of automation and intelligence. We highlight its use in customization, fraud detection, and healthcare diagnostics through fascinating real-world examples, highlighting its disruptive potential. The synergistic potential of these technologies, notably in predictive modeling and pattern recognition, is highlighted in the chapter’s conclusion. It also discusses the ethical issues surrounding the use of data and the proper application of AI, urging businesses to proceed in the data-driven world with caution and foresight. This chapter provides readers with a concise yet thorough overview of the influential trio of Big Data, Machine Learning, and Data Analytics, encouraging further investigation of their potential to reshape industries and spur innovation in the real world.

Artificial intelligence (AI), big data, and business analytics are the most commonly used and complete common sense cognitive tools in the ecospheres today, and they have garnered a lot of attention for their ability to influence organizational decision-making. With the use of these technologies, firms are able to provide valuable data and obtain answers that will improve their performance and provide them with a competitive advantage. A customer relationship management (CRM) and enterprise resource planning (ERP) business system, for example, can be integrated with AI solutions through the AI business platform paradigm. In addition to providing pattern analysis, big data analytics (BDA) enables automatic future event forecasting. BDA may revolutionize organizations and create new commercial prospects using AI. The goal is to highlight the preventive aspects of using AI and ML in conjunction with big data analytics (BDA) to pursuit digital platforms for business model innovation and dynamics. Additionally, a thorough assessment of the literature has been provided with an emphasis on the necessity of business transformation, the function of BDA, and the role of AI. One particular case study namely Big Mart Sales forecasting was discussed, compared and analyzed in the context of business transformation. The chapter discusses the possible obstacles to firms implementing AI and BDA. It will offer firms a roadmap for utilizing AI and BDA to generate commercial value.