Ai Based Predictive Analytics

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Ai Based Predictive Analytics

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Author : Minghai Zheng
language : en
Publisher: Independently Published
Release Date : 2023-06-02

Ai Based Predictive Analytics written by Minghai Zheng and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-02 with categories.

1. #DataDrivenDecisions #PredictiveAnalytics Unlock the power of data-driven decisions with AI-based predictive analytics! This book provides the insights and tools you need to make informed decisions based on data. 2. #AIinBusiness #PredictiveModeling Stay ahead of the competition with AI-based predictive analytics. Learn how to implement predictive modeling in your business with this must-read book. 3. #BigDataInsights #DecisionMaking Want to make better decisions based on big data insights? Look no further than "AI-Based Predictive Analytics." This book is your ultimate guide to leveraging the power of AI for predictive analytics. 4. #MachineLearning #DataAnalysis Discover the latest machine learning techniques for data analysis with "AI-Based Predictive Analytics." This book will help you make sense of complex data sets and turn them into actionable insights. 5. #BusinessIntelligence #AIinAction Maximize your business intelligence capabilities with AI-based predictive analytics. Get your copy of this book to learn how to implement these powerful strategies in your organization. In today's data-driven world, predictive analytics has emerged as a powerful tool for organizations looking to make informed decisions based on data. By analyzing historical data and identifying patterns, predictive analytics enables businesses to predict future outcomes and take proactive measures to improve outcomes. With the rise of artificial intelligence (AI), predictive analytics has become even more sophisticated, offering new ways to analyze data and uncover insights that were previously impossible to obtain. The book "AI-Based Predictive Analytics: Empowering Data-Driven Decisions" provides a comprehensive guide on how to implement AI-based predictive analytics strategies in your organization. Whether you're an analyst, data scientist, or business leader, this book will provide you with practical insights and tools to improve decision-making and drive success. In this book, we'll explore various ways in which AI can be used in predictive analytics. We'll discuss the latest machine learning techniques, including deep learning, natural language processing, and regression analysis. Additionally, we'll examine case studies of successful implementations of AI-based predictive analytics in different industries, along with the benefits and challenges associated with these implementations. Whether you're looking to reduce costs, improve efficiency, or enhance customer experiences, this book will provide you with the knowledge and tools needed to achieve your goals. The chapters that follow will delve deeper into specific topics related to AI-based predictive analytics, providing you with a comprehensive guide for implementing these strategies in your organization. MingHai Zheng is the founder of zhengpublishing.com and lives in Wuhan, China. His main publishing areas are business, management, self-help, computers and other emerging foreword fields.

Ai Based Data Analytics

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Author : Kiran Chaudhary
language : en
Publisher: CRC Press
Release Date : 2023-12-29

Ai Based Data Analytics written by Kiran Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-29 with Computers categories.

This book covers various topics related to marketing and business analytics. It explores how organizations can increase their profits by making better decisions in a timely manner through the use of data analytics. This book is meant for students, practitioners, industry professionals, researchers, and academics working in the field of commerce and marketing, big data analytics, and organizational decision-making. Highlights of the book include: The role of Explainable AI in improving customer experiences in e-commerce Sentiment analysis of social media Data analytics in business intelligence Federated learning for business intelligence AI-based planning of business management An AI-based business model innovation in new technologies An analysis of social media marketing and online impulse buying behaviour AI-Based Data Analytics: Applications for Business Management has two primary focuses. The first is on analytics for decision-making and covers big data analytics for market intelligence, data analytics and consumer behavior, and the role of big data analytics in organizational decision-making. The book’s second focus is on digital marketing and includes the prediction of marketing by consumer analytics, web analytics for digital marketing, smart retailing, and leveraging web analytics for optimizing digital marketing strategies.

Ai For Predictive Analytics

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Author : GREYSON. CHESTERFIELD
language : en
Publisher: Independently Published
Release Date : 2025-03-16

Ai For Predictive Analytics written by GREYSON. CHESTERFIELD and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-16 with Computers categories.

AI for Predictive Analytics: Forecasting with Machine Learning is your comprehensive guide to using artificial intelligence (AI) and machine learning (ML) for analyzing historical data and predicting future trends. This book explores the powerful role of AI in predictive analytics and how it is transforming industries like finance, healthcare, retail, and beyond by turning data into actionable insights. With clear explanations, practical examples, and hands-on tutorials, you'll learn how to apply AI and ML algorithms to forecast trends, make data-driven decisions, and optimize business operations. Whether you're a data scientist, analyst, or business leader, this book will equip you with the tools to leverage predictive analytics for success in your industry. Inside, you'll discover: Introduction to Predictive Analytics: Understand the basics of predictive analytics and the role AI plays in analyzing historical data to forecast future outcomes. Learn how predictive modeling and forecasting are applied in real-world scenarios. Machine Learning Algorithms for Forecasting: Dive into the machine learning algorithms used for predictive analytics, such as linear regression, decision trees, random forests, and neural networks. Learn how these algorithms help identify patterns and make predictions based on past data. Preparing Data for Predictive Modeling: Learn the importance of data cleaning, preprocessing, and feature engineering in building accurate predictive models. Discover techniques for handling missing data, outliers, and scaling data for machine learning algorithms. Time Series Forecasting: Explore the principles of time series analysis and how machine learning can be applied to predict trends over time. Learn how to work with time series data, including seasonal and trend components, and build models for accurate forecasting. Predictive Analytics in Finance: Discover how AI and machine learning are used in the finance industry for credit scoring, risk management, stock market prediction, and fraud detection. Learn how to apply predictive models to financial datasets for better decision-making. Predictive Analytics in Healthcare: Understand how predictive analytics is revolutionizing healthcare by forecasting patient outcomes, disease progression, and treatment efficacy. Learn how AI models are used in diagnostics, personalized medicine, and resource allocation. Evaluating and Improving Predictive Models: Learn how to evaluate the accuracy of predictive models using performance metrics like RMSE, MAE, and R-squared. Discover techniques for tuning models and improving their predictive power through cross-validation and hyperparameter optimization. Advanced Techniques in Predictive Analytics: Delve into advanced machine learning methods, such as ensemble learning, deep learning, and reinforcement learning, and understand how these techniques can be applied to more complex predictive tasks. Deploying Predictive Models in Production: Learn how to deploy machine learning models into production environments, manage model performance over time, and update models as new data becomes available. Ethical Considerations in Predictive Analytics: Explore the ethical implications of predictive analytics, including data privacy, algorithmic bias, and transparency in decision-making. Understand the importance of fairness and accountability in AI-driven predictions. By the end of this book, you'll be well-equipped to use AI and machine learning for predictive analytics, forecast trends, and make data-driven decisions in industries like finance, healthcare, and more.

Data Driven Modelling And Predictive Analytics In Business And Finance

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Author : Alex Khang
language : en
Publisher: CRC Press
Release Date : 2024-07-24

Data Driven Modelling And Predictive Analytics In Business And Finance written by Alex Khang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Computers categories.

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

Artificial Intelligence For Business Analytics

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Author : Felix Weber
language : en
Publisher: Springer Nature
Release Date : 2023-03-01

Artificial Intelligence For Business Analytics written by Felix Weber and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Computers categories.

While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

The 7th International Conference Nanotechnology Gtunano

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Author : Levan Chkhartishvili
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2025-12-03

The 7th International Conference Nanotechnology Gtunano written by Levan Chkhartishvili and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-03 with Technology & Engineering categories.

Selected peer-reviewed extended articles based on abstracts presented at the 7th International Conference “Nanotechnology” (GTUnano2024) Aggregated Book

Aircraft Management Activities A Systematic Review

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Author : Muflaha Jafar
language : en
Publisher: GRIN Verlag
Release Date : 2024-10-10

Aircraft Management Activities A Systematic Review written by Muflaha Jafar and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Business & Economics categories.

Master's Thesis from the year 2024 in the subject Business economics - Project Management, London Metropolitan University, language: English, abstract: This systematic review seeks to answer the questions regarding the development process, efficiency, issues and trends in the application of aircraft maintenance and management approaches. With ongoing changes in regulatory requirements, technological development and cost control remaining critical issues for the aviation industry, it is therefore imperative to gain an appreciation of the evolution of maintenance practices. The review is based on the collection of secondary qualitative articles from various peer-reviewed papers and industry reports starting from the year 2000 The main themes explored in the review include historical overview, change from reactive to proactive maintenance techniques, preventive, predictive and condition based maintenance techniques and strategies. The work shows that there is a rather sharp shift from what the authors call the reactive maintenance paradigm, where the primary objective is to repair failed assets, to the so-called smart maintenance, which is based on the use of big data and analytics, AI, robotics, digital twins, and the like. Among new effective methodologies we can single out predictive and condition-based maintenance, which, taking advantage of real-time data analytics and sensor technologies, proved their effectiveness in terms of reducing the time spent on failures, costs, and increasing safety in comparison with previous methods.

Data Analytics And Artificial Intelligence For Predictive Maintenance In Smart Manufacturing

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Author : Amit Kumar Tyagi
language : en
Publisher: CRC Press
Release Date : 2024-10-23

Data Analytics And Artificial Intelligence For Predictive Maintenance In Smart Manufacturing written by Amit Kumar Tyagi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-23 with Computers categories.

Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.

Leading With Ai And Analytics Build Your Data Science Iq To Drive Business Value

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Author : Eric Anderson
language : en
Publisher: McGraw Hill Professional
Release Date : 2020-11-23

Leading With Ai And Analytics Build Your Data Science Iq To Drive Business Value written by Eric Anderson and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-23 with Business & Economics categories.

Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.

Data Science And Predictive Analytics

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Author : Ivo D. Dinov
language : en
Publisher:
Release Date : 2023

Data Science And Predictive Analytics written by Ivo D. Dinov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in this textbook address specific knowledge gaps, resolve educational barriers, and mitigate workforce information readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical foundations, modern computational methods, advanced data science techniques, model-based machine learning (ML), model-free artificial intelligence (AI), and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build the foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. Individual modules and complete end-to-end pipeline protocols are available as functional R electronic markdown notebooks. These workflows support an active learning platform for comprehensive data manipulation, sophisticated analytics, interactive visualization, and effective dissemination of open problems, current knowledge, scientific tools, and research findings. This Second Edition includes new material reflecting recent scientific and technological progress and a substantial content reorganization to streamline the covered topics. Featured are learning-based strategies utilizing generative adversarial networks (GANs), transfer learning, and synthetic data generation. There are complete end-to-end examples of ML/AI training, prediction, and assessment using quantitative, qualitative, text, and imaging datasets. This textbook is suitable for self-learning and instructor-guided course training. It is appropriate for upper division and graduate-level courses covering applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide spectrum of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory and funding agencies.