This book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.
Author(s): Frank Acito
Publisher: Springer Nature Switzerland
Year: 2024
Language: English
Pages: 317
Cover
Front Matter
1. Introduction to Analytics
2. Problem Definition
3. Introduction to KNIME
4. Data Preparation
5. Dimensionality Reduction
6. Ordinary Least Squares Regression
7. Logistic Regression
8. Classification and Regression Trees
9. Naïve Bayes
10. k Nearest Neighbors
11. Neural Networks
12. Ensemble Models
13. Cluster Analysis
14. Communication and Deployment
Back Matter