Predictive Analytics with KNIME

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.

Chapter 1 Introduction to analytics. - Chapter 2 Problem definition. - Chapter 3 Introduction to KNIME. - Chapter 4 Data preparation. - Chapter 5 Dimensionality reduction and feature extraction. - Chapter 6 Ordinary least squares regression. - Chapter 7 Logistic regression. - Chapter 8 Decision and regression trees. - Chapter 9 Naïve Bayes. - Chapter 10 k nearest neighbors. - Chapter 11 Neural networks. - Chapter 12 Ensemble models. - Chapter 13 Cluster analysis. - Chapter 14 Communication and deployment