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81860539
Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner
товар №:
81860539
UZS 720336
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What Stands Out
Comprehensive Guide
This book offers in-depth insights into predictive analytics and data mining concepts, providing readers with a complete understanding of RapidMiner tools and techniques for effective data analysis.
Practical Applications
Focusing on real-world scenarios, it demonstrates the application of data mining concepts using RapidMiner, ensuring readers can apply theoretical knowledge to practical challenges in various industries.
User-Friendly Approach
Designed for both beginners and experienced users, it presents complex topics in an approachable manner, complete with examples and exercises, making it easy to learn and master predictive analytics.
Информация о продукте
| Publisher | Morgan Kaufmann |
| Publication date | December 17, 2014 |
| Edition | 1st |
| Language | English |
| Print length | 448 pages |
| ISBN-10 | 0128014601 |
| ISBN-13 | 978-0128014608 |
| Item Weight | 2.04 pounds (930 grams) |
| Dimensions | 7.5 x 1.01 x 9.25 inches (19.1 x 2.6 x 23.5 cm) |
Who Should Buy?
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Data Analysts
Ideal for data analysts looking to enhance skills in predictive analytics using RapidMiner software effectively.
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Students and Learners
Great resource for students seeking practical knowledge in data mining and predictive analytics techniques.
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Business Professionals
Useful for business professionals aiming to leverage data insights for strategic decision-making in their organizations.
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Total Beginners
Not suitable for individuals with no prior knowledge of statistics or data analysis concepts.
ОПИСАНИЕ ТОВАРА
Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner
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Intelligence & Semantics Editorial Review
**** “Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner” by Vijay Kotu and Bilshan Deshpande presents a comprehensive introduction to data mining and predictive analytics, tailored particularly for novices and individuals who may find mathematics challenging. The authors effectively combine theoretical concepts with practical applications, utilizing RapidMiner as the central software tool for real-world implementation. Many customers commend the book for its wealth of illustrations and clear explanations, particularly noting the examples such as the iconic Iris dataset, which clarify complex notions. Readers appreciate the book's structured approach, allowing even those new to the field to grasp the intricacies of predictive analytics. While some users express a desire for a more in-depth exploration of RapidMiner earlier in the book, they recognize its substantial value for anyone involved in ecommerce or interested in leveraging data analysis to drive business decisions. However, some critiques focus on the need for a deeper exploration of model evaluation and results interpretation, suggesting that these areas could be addressed more thoroughly. Overall, Kotu and Deshpande’s expertise shines through, making the book a beneficial resource for both aspiring data scientists and seasoned analysts seeking to enhance their skill set with practical tools. **
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Плюсы
- Clear explanations suited for novices and those without a strong math background.
- Abundant illustrations and worked examples enhance understanding.
- Well-structured format that allows readers to learn through hands-on exercises.
- Effective integration of theoretical concepts with practical application using RapidMiner.
- Strong recommendation from users involved in data-driven decision-making roles.
Минусы
- Some readers wanted more application of RapidMiner concepts earlier in the text.
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Особенности и преимущества
- Learn Predictive Analysis and Data Mining in an easy-to-understand framework.
- Practice concepts with the open-source tool, RapidMiner.
- Ideal for business users, data analysts, and BI professionals.
- Master various data mining techniques for effective analytics processes.
- Quickly implement over two dozen powerful algorithms for predictive analytics.
- Access comprehensive implementation files from the book's companion site.
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