Utilizing Rapidminer Python And R For Data Mining Applications
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Utilizing Rapidminer Python And R For Data Mining Applications
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Author : Ramjan, Sarawut
language : en
Publisher: IGI Global
Release Date : 2025-05-02
Utilizing Rapidminer Python And R For Data Mining Applications written by Ramjan, Sarawut and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Computers categories.
In data mining, powerful tools like RapidMiner, Python, and R revolutionize how organizations gain valuable insights from large amounts of data. RapidMiner offers a visual interface for designing data workflows, making it ideal for both beginners and advanced practitioners. Python provides an environment for automating and customizing data mining tasks, while R is used for its statistical capabilities and packages for advanced analytics. Together, these tools empower data scientists and analysts to apply machine learning algorithms, statistical models, and data preprocessing techniques efficiently, facilitating deeper understanding and data-driven decision-making across industries. Utilizing RapidMiner, Python, and R for Data Mining Applications explores the integration and application of these three powerful tools in the context of real-world data mining tasks. It delves into the strengths and features of each tool, showcasing how they can be leveraged individually or in combination to handle various stages of the data mining pipeline. This book covers topics such as data clustering, software installation, and programming languages, and is a useful resource for engineers, business owners, academicians, researchers, and data scientists.
Utilizing Rapidminer Python And R For Data Mining Applications
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Author : Sarawut Ramjan
language : en
Publisher:
Release Date : 2025
Utilizing Rapidminer Python And R For Data Mining Applications written by Sarawut Ramjan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Utilizing Rapidminer Python And R For Data Mining Applications
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Author : SARAWUT. RAMJAN
language : en
Publisher:
Release Date : 2025
Utilizing Rapidminer Python And R For Data Mining Applications written by SARAWUT. RAMJAN and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Data Mining And Business Intelligence
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Author : Dr. Jyotiranjan Hota
language : en
Publisher: BPB Publications
Release Date : 2025-05-20
Data Mining And Business Intelligence written by Dr. Jyotiranjan Hota and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.
DESCRIPTION Data mining is crucial in business intelligence as it enables organizations to extract valuable insights and patterns from vast datasets, ultimately supporting informed decision-making, enhancing operational efficiency, and driving strategic growth. Validations, model building and interpretations are accomplished through databases, data warehouses, various supervised and unsupervised algorithms, tools for data modeling, descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics to ensure accurate decision-making. This book systematically explores the core concepts and techniques of data mining and business intelligence. It begins by introducing fundamental principles and key methodologies, including regression, classification, association rule mining, and clustering. The text progresses to cover business intelligence architectures, data warehousing, and essential practices like data modeling, dashboard design, and data visualization using tools like Power BI. Furthermore, it delves into advanced topics such as text mining, big data analytics, and the ethical considerations surrounding data mining and business intelligence, ensuring a well-rounded understanding. Upon completing this book, readers will be competent in understanding various pre-processing techniques, applying appropriate data mining algorithms to large data sets, and conducting data analysis and interpretation to derive meaningful insights. They will also gain skills in data modeling and visualization to effectively communicate findings to business leaders and policymakers. Additionally, readers will develop an understanding of ethical considerations in data practices. WHAT YOU WILL LEARN ● Conducting pre-processing of data, applying appropriate algorithm to generate model summary and communicating the result effectively. ● Master data mining, BI principles, regression, classification, association rules, and clustering. ● Design BI architectures, ETL processes, data warehouses, and effective data visualizations. ● Utilize Power BI for data modeling, dashboard design, and create compelling data visualizations. ● Explore text mining, big data analytics, and the ethical dimensions of data practices. ● Implement regression, classification, association rule mining, and clustering techniques. ● Develop expertise in data mining, business intelligence, and ethical data application. WHO THIS BOOK IS FOR This textbook is written for a wide range of audiences, including professionals such as data analysts, business managers, IT specialists, analytics professionals, and researchers seeking to enhance their understanding of data-driven decision-making. It is also valuable for students who want to establish foundational knowledge in data mining and business intelligence. TABLE OF CONTENTS 1. Introduction to Data Mining and Business Intelligence 2. Regression and Classification Techniques with Applications 3. Concept and Application of Association Rule Mining Algorithm 4. Clustering 5. Introduction to Business Intelligence 6. Business Intelligence Architecture, Query and Reporting Practices 7. Advanced Data Mining and Business Intelligence Techniques 8. Data Mining and Business Intelligence Ethical Framework
Data Science And Machine Learning For Non Programmers
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Author : Dothang Truong
language : en
Publisher: CRC Press
Release Date : 2024-02-23
Data Science And Machine Learning For Non Programmers written by Dothang Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Business & Economics categories.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
Proceedings Of The 3rd International Conference On Intelligent And Interactive Computing 2021 Utem Press
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Author : Sarni Suhaila Rahim
language : en
Publisher: UTeM Press
Release Date : 2021-09-01
Proceedings Of The 3rd International Conference On Intelligent And Interactive Computing 2021 Utem Press written by Sarni Suhaila Rahim and has been published by UTeM Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Computers categories.
The 3rd International Conference on Intelligent and Interactive Computing 2021 (IIC 2021) was held virtually at Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia, on 9 September 2021. The event was jointly organized by the Department of Interactive Media and Department of Intelligent Computing and Analytics, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), with the theme ‘Empowering the World with Intelligent and Immersive Computing towards Smart Solutions’. This open access e-proceedings contains a compilation of 38 selected papers from the IIC 2021. The technical committees received a great response for submissions from various area including computational intelligence, data analytics, robotics and automation, multimedia and immersive technologies, education 4.0 and others. We hope that this proceeding will serve as a valuable reference for researchers. The event has achieved its aim which is to gather academic scholars and industry practitioners to share valuable knowledge and expertise in related disciplines. Moreover, it is hoped that this conference has opened up opportunities to explore recent advancements and challenges on selected research discipline. As the editors-in-chief, we are grateful and would like to convey our sincerest gratitude to the fellow review members for their effort in reviewing the submitted papers for this proceeding. We are thankful to all the authors for revising their papers according to the proceeding requirements. Also, we would like to express our thoughtful appreciation to the organizer of the IIC 2021.
Principles And Theories Of Data Mining With Rapidminer
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Author : Ramjan, Sarawut
language : en
Publisher: IGI Global
Release Date : 2023-05-09
Principles And Theories Of Data Mining With Rapidminer written by Ramjan, Sarawut and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Computers categories.
The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.
Artificial Intelligence Trends In Intelligent Systems
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Author : Radek Silhavy
language : en
Publisher: Springer
Release Date : 2017-04-06
Artificial Intelligence Trends In Intelligent Systems written by Radek Silhavy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-06 with Technology & Engineering categories.
This book presents new methods and approaches to real-world problems as well as exploratory research that describes novel artificial intelligence applications, including deep learning, neural networks and hybrid algorithms. This book constitutes the refereed proceedings of the Artificial Intelligence Trends in Intelligent Systems Section of the 6th Computer Science On-line Conference 2017 (CSOC 2017), held in April 2017.
Learn R For Applied Statistics
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Author : Eric Goh Ming Hui
language : en
Publisher: Apress
Release Date : 2018-11-30
Learn R For Applied Statistics written by Eric Goh Ming Hui and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.
Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-28
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-28 with Computers categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learning A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.