Data mining for business analytics: concepts, techniques and applications in Python

Table of contents
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"

https://www.wiley.com/en-us/Data+Mining+for+Business+Analytics:+Concepts,+Techniques+and+Applications+in+Python-p-9781119549840