Business analytics with R and python
Olson, David L
Business analytics with R and python - Singapore Springer 2024 - x, 196 p. - AI for Risks .
Table of content:
Data Mining in Business
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 1-7
Data Mining Processes
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 9-21
Data Mining Software
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 23-40
Association Rules
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 41-62
Cluster Analysis
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 63-98
Regression Algorithms in Data Mining
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 99-124
Classification Tools
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 125-163
Variable Selection
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 165-179
Dataset Balancing
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 181-193
Correction to: Business Analytics with R and Python
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages C1-C1
Back Matter
Pages 195-196
[https://link.springer.com/book/10.1007/978-981-97-4772-6]
This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.
(https://link.springer.com/book/10.1007/978-981-97-4772-6)
9789819747719
Business--Data processing
Data mining
658.4 / OLS
Business analytics with R and python - Singapore Springer 2024 - x, 196 p. - AI for Risks .
Table of content:
Data Mining in Business
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 1-7
Data Mining Processes
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 9-21
Data Mining Software
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 23-40
Association Rules
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 41-62
Cluster Analysis
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 63-98
Regression Algorithms in Data Mining
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 99-124
Classification Tools
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 125-163
Variable Selection
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 165-179
Dataset Balancing
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages 181-193
Correction to: Business Analytics with R and Python
David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Pages C1-C1
Back Matter
Pages 195-196
[https://link.springer.com/book/10.1007/978-981-97-4772-6]
This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.
(https://link.springer.com/book/10.1007/978-981-97-4772-6)
9789819747719
Business--Data processing
Data mining
658.4 / OLS