Business Analytics with R and Python / by David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi.

Format:

Book

Author/Creator:

Olson, David L., 1944- author.

Wu, Desheng Dash, Author.

Luo, Cuicui, Author.

Nabavi, Majid, Author.

Series:

AI for Risks, 2731-6335

Language:

English

Subjects (All):

Business information services.

Business Information Systems.

Local Subjects:

Business Information Systems.

Physical Description:

1 online resource (201 pages)

Edition:

1st ed. 2024.

Place of Publication:

Springer Nature 2024

Summary:

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.

Contents:

Data Mining in Business

Data Mining Processes

Data Mining Software

Association Rules

Cluster Analysis.-Regression Algorithms in Data Mining

Classification Tools

Variable Selection

Dataset Balancing.

Notes:

Includes bibliographical references and index.

ISBN:

981-9747-72-4

OCLC:

1450839428