You will learn how to implement a variety of popular data mining algorithms in Python (a programming language - software development environment) to tackle business problems and opportunities.This is the first version of the python book series and it covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining, and network analysis. It also includes: A new co-author Freeman Dlamini, brings both experiences teaching business analytics courses using Python, and expertise in the application of machine learning methods.A new section on ethical issues in data miningMore than a dozen case studies demonstrating applications for the data mining techniques describedEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedData Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This book is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology."This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business-specific procedures such as social network analysis and text mining