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Hardcover. Etat : new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Etat : new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.