Data mining for business analytics : concepts techniques and applications in Python Foreword by Gareth James Foreword by Ravi Bapna Preface to the Python edition Overview of the data mining process Data visualization Dimension reduction Evaluating predictive performance Multiple linear regression k nearest neighbors (kNN) The naive Bayes classifier Classification and regression trees Logistic regression Neural nets Discriminant analysis Combining methods : ensembles and uplift modeling Association rules and collaborative filtering Cluster analysis Handling time series Regression based forecasting Smoothing methods Social network analytics Text mining Cases. This book supplies insightful detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting real world examples to build a theoretical and practical understanding of key data mining methods with a focus on analytics rather than programming. The book includes discussions of Python subroutines allowing readers to work hands on with the provided data. Throughout the book applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Topics covered include time series text mining and dimension reduction. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced and a related Web site features over two dozen data sets exercise solutions PowerPoint slides and case solutions