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269391617
Utilizing RapidMiner Python and R for Data Mining Applications
Item #:
269391617
NAD 4432
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- 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.
| Publisher | IGI Global |
| Publication date | 2 May 2025 |
| Language | English |
| Print length | 404 pages |
| ISBN-13 | 979-8369360026 |
| Dimensions | 17.78 x 2.11 x 25.4 cm |
Product Description
Utilizing RapidMiner Python and R for Data Mining Applications
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Features & Benefits
- Revolutionize insights from large datasets with RapidMiner's visual interface.
- Automate and customize data mining tasks effortlessly using Python.
- Leverage R's statistical capabilities for advanced analytics.
- Combine powerful tools to tackle all stages of the data mining pipeline.
- Ideal resource for data scientists, engineers, and business owners alike.
- Explore real-world applications through data clustering and programming techniques.
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