Data Mining and Predictive Analytics for Business Decisions : : A Case Study Approach / / Andres Fortino.

• VerfasserIn: Fortino, Andres,
• Place / Publishing House: Dulles, VA : : Mercury Learning and Information, , [2023]; ©2023
• Year of Publication: 2023
• Language: English
• Physical Description: 1 online resource (272 p.)

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc.

Table of Contents

Frontmatter -- Contents -- Preface -- Acknowledgments -- Chapter 1: Data Mining and Business -- Chapter 2: The Data Mining Process -- Chapter 3: Framing Analytical Questions -- Chapter 4: Data Preparation -- Chapter 5: Descriptive Analysis -- Chapter 6: Modeling -- Chapter 7: Predictive Analytics with Regression Models -- Chapter 8: Classification -- Chapter 9: Clustering -- Chapter 10: Time Series Forecasting -- Chapter 11: Feature Selection -- Chapter 12: Anomaly Detection -- Chapter 13: Text Data Mining -- Chapter 14: Working with Large Data Sets -- Chapter 15: Visual Programming -- Index